data mining assignment

Organization

Data Mining Programming Assignment

For the individual project, each student will write a program for discovering approximate functional dependencies in a data set, representing an instance of a universal relation.

Conceptual guidance for the project can be found in the lectures of April 3 and April 5, as well as this paper , particularly Section 4. The paper uses the AI term for functional dependencies — determinations — and you might be otherwise unfamiliar with the AI and machine learning terminology of the paper, but together with the lecture, I think you can follow Section 4.

In particular, you will write and test a collection of functions that learn approximate functional dependencies from data. The functions are to be written in PYTHON 3, and the top level function is

where DataFileName is the name of a CSV file in the program’s local directory, which is formatted as

There are M columns and N+1 rows to this file.

The columns of this file correspond to the M different attributes, say A, B, C, … that describe the universal relation. The first row (the zeroth row) gives the M attribute names used to describe the data. The remaining N rows are the N records (or tuples) in the instance of the relation. The value given in row i (i>=1) of column j is the value of attribute j (as given in the first row of attribute names) for tuple i

You are provided with a helper function that converts the CSV file to a list of lists , which you should call in one of your functions to convert the data into an internal representation that your code can use. You can, of course, convert this list of list into another data structure as well. A skeletal Python script (as a .txt file; save as .py file), along with the CSV helper function, is here . A very simple sample data set is here , which is only provided to check the read function — remember that designing a data set with predictable FDs is part of your task — we will supply some data sets later. (UPDATE: see data here )

In a real BIG-BIG-BIG-data mining implementation, you wouldn’t read the entire data set into main memory at the outset, but we are not experimenting with BIG-BIG-BIG data.

depth-limit , referred to as k in both lecture and Section 4.1 of the paper, is an integer that limits the depth of search through the space of domains of functional dependencies.

minimum-support , referenced on the last slide of April 3 lecture, is the threshold for identifying adequately approximate FDs (e.g., 0.95 in the lecture slide).

Your function, find-approximate-functional-dependencies , will return a list of tuples, where a tuple representing the FD A,B,C–>D (with support 0.98 — see lecture) is represented as ([A,B,C],D, 0.98) . Note that the first element of a tuple is a list of the attributes in the domain of the FD, and the second element of the tuple is a single attribute name. An example result of the find-approximate-functional-dependencies (with depth-limit = 3, minimal-support=0.90) is

[([A],C, 0.91), ([C, F],E, 0.97), ([A,B,C],D, 0.98), ([A, G, H],F, 0.92)]

Note that there are no domains over cardinality 3 (because depth-limit=3), and no FDs with less support than 0.90.

An example result of the find-approximate-functional-dependencies (with depth-limit = 2, minimal-support=0.80) is

[([A],C, 0.91), ([C],A, 0.89), ([F],G, 0.81), ([B,C],D, 0.90), ([B, E],C, 0.85), ([D, F],B, 0.81) ]

We (and you) will pretty print (pprint) your results and otherwise inspect them for correctness.

You will upload one .py (.txt) file with your Python 3 code to Brightspace. You may not use libraries (though this restriction may change shortly). If you

for both on data sets you will be given ahead of time and those we use to grade;
nicely format and comment your code with comprehensible and informative function header comments;

then you will receive an A- score (90%). If, in addition, you implement some efficiency enhancement, or some other addition found in April 12 lecture, and explain it clearly in comments at the top of the submission file, then you can receive up to 100%.

Search for:

Recent Comments

No categories
Entries RSS
Comments RSS
WordPress.org

Pictograms of clouds, graphs and pie chart

Updated : 28 June 2024 Contributor : Jim Holdsworth

Data mining is the use of machine learning and statistical analysis to uncover patterns and other valuable information from large data sets.

Given the evolution of machine learning (ML), data warehousing , and the growth of big data , the adoption of data mining, also known as knowledge discovery in databases (KDD), has rapidly accelerated over the last decades. However, while this technology continuously evolves to handle data at a large scale, leaders still might face challenges with scalability and automation .

The data mining techniques that underpin data analyses can be deployed for two main purposes. They can either describe the target data set or they can predict outcomes by using machine learning algorithms.

These methods are used to organize and filter data, surfacing the most useful information, from fraud to user behaviors, bottlenecks and even security breaches. Using ML algorithms and artificial intelligence (AI) enables automation of the analysis, which can greatly speed up the process.

When combined with data analytics and visualization tools, such as Apache Spark , data mining software is becoming more straightforward and extracting relevant insights can be gained more quickly than ever. Advances in AI continue to expedite adoption across industries.

Learn how to confidently incorporate generative AI and machine learning into your business.

Discover hidden insights and trends : Data mining takes raw data and finds order in the chaos: seeing the forest for the trees. This can result in better-informed planning across corporate functions and industries, including advertising, finance, government , healthcare, human resources (HR), manufacturing, marketing, research, sales and supply chain management (SCM) .

Save budget : By analyzing performance data from multiple sources, bottlenecks in business processes can be identified to speed resolution and increase efficiency.

Solve multiple challenges : Data mining is a versatile tool. Data from almost any source and any aspect of an organization can be analyzed to discover patterns and better ways of conducting business. Almost every department in an organization that collects and analyzes data can benefit from data mining.

Complexity and risk : Useful insights require valid data, plus experts with coding experience. Knowledge of data mining languages including Python, R and SQL is helpful. An insufficiently cautious approach to data mining might result in misleading or dangerous results. Some consumer data used in data mining might be personally identifiable information (PII) which should be handled carefully to avoid legal or public relations issues.

Cost : For the best results, a wide and deep collection of data sets is often needed. If new information is to be gathered by an organization, setting up a data pipeline might represent a new expense. If data needs to be purchased from an outside source, that also imposes a cost.

Uncertainty : First, a major data mining effort might be well run, but produce unclear results, with no major benefit. Or inaccurate data can lead to incorrect insights, whether incorrect data was selected or the preprocessing was mishandled. Other risks include modeling errors or outdated data from a rapidly changing market.

Another potential problem is results might appear valid but are in fact random and not to be trusted. It’s important to remember that “correlation is not causation.” A famous example of “data dredging”—seeing an apparent correlation and overstating its importance—was recently presented by blogger Tyler Vigen: “The price of Amazon.com stock closely matches the number of children named ‘Stevie’ from 2002 to 2022.” 1 But, of course, the naming of Stevies did not influence the stock price or vice versa. Data mining applications find the patterns, but human judgment is still significant.

Data mining is the overall process of identifying patterns and extracting useful insights from big data sets. This can be used to evaluate both structured and unstructured data to identify new information and is commonly used to analyze consumer behaviors for marketing and sales teams. For example, data mining methods can be used to observe and predict behaviors, including customer churn, fraud detection , market basket analysis and more.

Text mining —also known as text data mining—is a sub-field of data mining, intended to transform unstructured text into a structured format to identify meaningful patterns and generate novel insights. The unstructured data might include text from sources including social media posts, product reviews, articles, email or rich media formats such as video and audio files. Much of the publicly available data around the world is unstructured, making text mining a valuable practice.

Process mining sits at the intersection of business process management (BPM) and data mining. Process mining provides a way to apply algorithms to event log data to identify trends, patterns and details of how processes unfold. Process mining applies data science to discover bottlenecks, and then validate and improve workflows .

BPM generally collects data more informally through workshops and interviews and then uses software to document that workflow as a process map . Since the data that informs these process maps is often qualitative, process mining brings a more quantitative approach to a process problem, detailing the actual process through event data.

Information systems, such as enterprise resource planning (ERP) or customer relationship management (CRM) tools, provide an audit trail of processes from log data. Process mining uses this data from IT systems to assemble a process model or process graph. From there, organizations can examine the end-to-end process with the details and any variations outlined.

The data mining process involves several steps from data collection to visualization to extract valuable information from large data sets. Data mining techniques can be used to generate descriptions and predictions about a target data set.

Data scientists or business intelligence (BI) specialists describe data through their observations of patterns, associations and correlations. They also classify and cluster data through classification and regression methods, and identify outliers for use cases, such as spam detection.

Data mining usually includes five main steps: setting objectives, data selection, data preparation, data model building, and pattern mining and evaluating results.

1. Set the business objectives: This can be the hardest part of the data mining process, and many organizations spend too little time on this important step. Even before the data is identified, extracted or cleaned, data scientists and business stakeholders can work together to define the precise business problem, which helps inform the data questions and parameters for a project. Analysts might also need to do more research to fully understand the business context.

2. Data selection: When the scope of the problem is defined, it is easier for data scientists to identify which set of data will help answer the pertinent questions to the business. They and the IT team can also determine where the data should be stored and secured.

3. Data preparation: The relevant data is gathered and cleaned to remove any noise, such as duplicates, missing values and outliers. Depending on the data set, an additional data management step might be taken to reduce the number of dimensions, as too many features can slow down any subsequent computation.

Data scientists look to retain the most important predictors to help ensure optimal accuracy within any model. Responsible data science means thinking about the model beyond the code and performance, and it is hugely impacted by the data being used and how trustworthy it is.

4. Model building and pattern mining: Depending on the type of analysis, data scientists might investigate any trends or interesting data relationships, such as sequential patterns, association rules or correlations. While high-frequency patterns have broader applications, sometimes the deviations in the data can be more interesting, highlighting areas of potential fraud. Predictive models can help assess future trends or outcomes. In the most sophisticated systems, predictive models can make real-time predictions for rapid responses to changing markets.

Deep learning algorithms might also be used to classify or cluster a data set depending on the available data. If the input data is labeled (such as in supervised learning ), a classification model might be used to categorize data, or alternatively, a regression might be applied to predict the likelihood of a particular assignment. If the data set isn’t labeled (that is, unsupervised learning ), the individual data points in the training set are compared to discover underlying similarities, clustering them based on those characteristics.

5. Evaluation of results and implementation of knowledge: When the data is aggregated, it can then be prepared for presentation, often by using data visualization techniques, so that the results can be evaluated and interpreted. Ideally, the final results are valid, novel, useful and understandable. When these criteria are met, decision-makers can use this knowledge to implement new strategies, achieving their intended objectives.

Here are some of the most popular types of data mining:

Association rules: An association rule is an if/then, rule-based method for finding relationships between variables in a data set. The strengths of relationships are measured by support and confidence. The confidence level is based on how often the if or then statements are true. The support measure is how often the related elements are shown in the data.

These methods are frequently used for market basket analysis, enabling companies to better understand the relationships between different products, such as those that are frequently purchased together. Understanding customer habits enables businesses to develop better cross-selling strategies and recommendation engines. Classification : Classes of objects are predefined, as needed by the organization, with definitions of the characteristics that the objects have in common. This enables the underlying data to be grouped for easier analysis.

For example, a consumer product company might examine its couponing strategy by reviewing past coupon redemptions together with sales data, inventory stats and any consumer data on hand to find the best future campaign strategy. Clustering : Closely related to classification, clustering reports similarities, but then also provides more groupings based on differences. Preset classifications for a soap manufacturer might include detergent, bleach, laundry softener, floor cleaner and floor wax; while clustering might create groups including laundry products and floor care. Decision tree: This data mining technique uses classification or regression analytics to classify or predict potential outcomes based on a set of decisions. As the decision tree name suggests, it uses a tree-like visualization to represent the potential outcomes of these decisions.

K-nearest neighbor (KNN): Also known as the KNN algorithm, K-nearest neighbor is a nonparametric algorithm that classifies data points based on their proximity and association to other available data. This algorithm assumes that similar data points are found near each other. As a result, it seeks to calculate the distance between data points, usually through Euclidean distance, and then it assigns a category based on the most frequent category or average. Neural networks: Primarily used for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output.

If that output value exceeds the set threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, making adjustments based on the loss function through the process of gradient descent. When the cost function is at or near zero, an organization can be confident in the model’s accuracy to yield the correct answer.

Predictive analytics: By combining data mining with statistical modeling techniques and machine learning, historical data can be analyzed by using predictive analytics to create graphical or mathematical models intended to identify patterns, forecast future events and outcomes, and identify risks and opportunities. Regression analysis : This technique discovers relationships in data by predicting outcomes based on predetermined variables. This can include decision trees and multivariate and linear regression . Results can be prioritized by the closeness of the relationship to help determine what data is most or least significant. An example would be for a soft drink manufacturer to estimate the needed inventory of drinks before the arrival of predicted hot summer weather.

Data mining techniques are widely adopted by business intelligence and data analytics teams, helping them extract knowledge for their organization and industry. Some data mining use cases include:

Anomaly detection While frequently occurring patterns in data can provide teams with valuable insights, observing data anomalies is also beneficial, assisting organizations with fraud detection , network intrusions and product defects. While this is a well-known use case within banking and other financial institutions, SaaS-based companies have also started to adopt these practices to eliminate fake user accounts from their data sets. Anomaly detection can also be an opportunity to find new and novel strategies or target markets that have been overlooked in the past.

Assess risk Organizations can more accurately locate and determine the scale of risk with data mining. Patterns and anomalies can be uncovered in the cybersecurity , finance and legal fields to pinpoint oversights or threats.

Focus on target markets By searching across multiple databases to find close relationships, data mining can accurately connect behaviors and customer backgrounds with sales of specific items. This can enable more targeted campaigns to help boost sales.

Improve customer service Customer issues can be discovered and fixed sooner if the full sum of customer actions—on-site, online, over mobile apps or on a telephone—can be reviewed with data mining. Customer service agents can have access to more complete and insightful information on the customers they serve. Increase equipment uptime Operational data can be mined from industrial equipment that can help predict future performance and downtime, and enable the planning of protective maintenance. Operational optimization Process mining uses data mining techniques to reduce costs across operational functions, enabling organizations to run more efficiently. This practice can help to identify costly bottlenecks and improve decision-making for business leaders.

Customer service Data mining can create a richer data source for customer service by helping to determine which factors most please the customers and what factors cause friction or dissatisfaction.

Education Educational institutions have started to collect data to understand their student populations and which environments are conducive to success. With courses often using online platforms, they can use various dimensions and metrics to observe and evaluate performance, such as keystrokes, student profiles, classes attended and time spent.

Finance When researching risk, financial institutions and banks often want to cast a wide net, to capture any factors that might negatively impact cash flow and retrieval. Data mining tools can be useful in finding and weighing a combination of factors that indicate a good or bad risk.

Healthcare Data mining is a useful tool for the diagnosis of medical conditions—including the reading of scans and images—and then assists in the suggestion of beneficial treatments. Human resources Organizations can gain new insights into employee performance and satisfaction by analyzing multiple factors and finding patterns. Data can include start date, tenure, promotions, salary, training, peer performance, work delivery, use of benefits and travel. Manufacturing From raw materials to final delivery, all aspects of the manufacturing process can be analyzed to improve performance. What is the cost of materials and are there options? How efficient is production? Where are the bottlenecks? What are the quality issues and where do they arise, both internally and with customers?

Retail By mining customer data and actions, retailers can identify the most productive campaigns, pricing, promotions, special product offers and successful cross-sells and up-sells.

Sales and marketing Companies collect massive amounts of data about their customers and prospects. By observing consumer demographics, media responses and customer behavior, companies can use data to optimize their marketing campaigns, improving segmentation and targeting and customer loyalty programs, all helping to yield higher return on investment (ROI) on marketing efforts. Predictive analyses can also help teams set expectations with their stakeholders, providing yield estimates for any increases or decreases in marketing investment.

Social media Analysis of user data can help uncover new editorial opportunities or new sources of advertising revenue for specific target audiences.

Supply chain management (SCM) Using data mining, product managers can better predict demand, gear up production, adjust providers or adapt marketing efforts. Supply chain managers can better plan shipping and warehousing.

Find critical answers and insights from your business data by using AI-powered enterprise search technology.

A fully managed, elastic cloud data warehouse built for high-performance analytics and AI.

Import large volumes of data from several disparate sources to reveal hidden data patterns and trends.

Identify patterns and trends with predictive analytics and key techniques.

Explore how to mitigate your own biases when creating machine learning models.

Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. Build AI applications in a fraction of the time with a fraction of the data.

¹ " Spurious Correlations " (link resides outside ibm.com), Tyler Vigen.

Assignment 1: Data Set Selection/Preparation

Start date 17 january, due 24 january beginning of class..

Your task for this assignment is to identify and characterize a data set. It would be best if you have some domain experience, as this will help with data preparation. Answer the following questions about the data:

What the data is about.
What type of benefit you might hope to get from data mining.
What type of data mining (classification, clustering, etc.) you think would be relevant. For each, illustrate with an example, e.g., if you think clustering is relevant, describe what you think a likely cluster might contain and what the real-world meaning would be.
Name one type of data mining that you think would not be relevant, and describe briefly why not.
Are there problems with the data?
What might be an appropriate response to the quality issues.
What would an appropriate smoothing or generalization technique be?
What is an appropriate normalization or data reduction technique?

Exercises from the Book

Complete the following exercises from the book.

Turning in assignment

Browse Course Material

Course info.

Prof. Nitin Patel

Departments

Sloan School of Management

As Taught In

Information Technology
Data Mining

Learning Resource Types

Assignments.

You are leaving MIT OpenCourseWare

Data Mining

G22.3033-002

Dr. Jean-Claude Franchitti

New York University

Computer Science Department

Courant Institute of Mathematical Sciences

Session 2: Assignment #1

Course Title: Data Mining Course Number: G22.3033-002

Instructor : Jean-Claude Franchitti Session: 1

I. Due Thursday February 4, 2010, at the beginning of class.

II. Objectives

Understand Data Mining context and basic concepts.

III. References

Data Mining: Concepts and Techniques (2 nd Edition) by Jiawei Han and Micheline Kamber – Chapter 1.
Slides and handouts posted on the course Web site

IV. Software Required

Microsoft Word.
Win Zip as necessary.

V. Assignment

1. Question 1: Textbook exercise 1.5.

2. Question 2: Textbook exercise 1.7.

3. Question 3: Textbook exercise 1.10.

4. Question 4: Textbook exercise 1.11.

5. Question 5: Textbook exercise 1.12.

6. Question 6: Textbook exercise 1.15.

7. Compile your answers to all questions above in a file using Microsoft Word. Save the file as a Word document . Name the file Assignment1.doc .

8. Email your assignment file to the grader.

VI. Deliverables

Electronic: Your assignment file must be emailed to the grader. The file must be created and sent by the beginning of class. After the class period, the program is late. The email clock is the official clock.
Written : Printout of the file. The cover page supplied on the next page must be the first page of your assignment file

Fill in the blank area for each field.

The sequence of the hardcopy submission is:

Cover sheet

2. Assignment answer sheet(s)

VII. Sample Cover Sheet:

Name ________________________ Date: ____________ (last name, first name ) Section: ___________

Assignment 1

Assignment Layout (25%)

o Assignment is neatly assembled on 8 1/2 by 11 paper .

o Cover page with your name (last name first followed by a comma then first name) and section number with a signed statement of independent effort is included.

o All questions are answered and correctly numbered. o File name is correct.

Answers to Individual Questions (15% per question)

o Assumptions provided when required.

Total in points: ___________________ Professor’s Comments:

Affirmation of my Independent Effort: _____________________________

(Sign here)

Data Science
Data Analysis
Data Visualization
Machine Learning
Deep Learning
Computer Vision
Artificial Intelligence
AI ML DS Interview Series
AI ML DS Projects series
Data Engineering
Web Scrapping
BCA 6th Semester Subjects and Syllabus (2023)

Computer Network Security

What is Network Security?
A Model for Network Security
IPSec Architecture
Web Security Considerations
System Security

Information System Analysis Design and Implementation

Differences between System Analysis and System Design
Activities involved in Software Requirement Analysis
Types of Feasibility Study in Software Project Development
System Design Tutorial
User Interface Design - Software Engineering
Computer Aided Software Engineering (CASE)
Object-Oriented Analysis and Design(OOAD)
Dynamic modelling in object oriented analysis and design
Software Project Management Complexities | Software Engineering
Scope of e-Business : B2B | B2C | C2C | Intra B-Commerce
Difference between Internet and Extranet
What is Extranet? Definition, Implementation, Features
What is an Intranet?
Meaning and Benefits of e-Banking

Knowledge Management

What is Business Intelligence?
Difference between Business Intelligence and Business Analytics
Difference between EIS and DSS
Data Mining Techniques

Data Mining Tutorial

Knowledge Management: Meaning, Concept, Process and Significance
BCA 1st Semester Syllabus (2023)
BCA 2nd Semester Syllabus (2023)
BCA 3rd Semester Syllabus (2023)
BCA 4th Semester Syllabus (2023)
BCA 5th Semester Syllabus (2023)
BCA Full Form
Bachelor of Computer Applications: Curriculum and Career Opportunity

Data Mining Tutorial covers basic and advanced topics, this is designed for beginner and experienced working professionals too. This Data Mining Tutorial help you to gain the fundamental of Data Mining for exploring a wide range of techniques.

Data Mining

What is Data Mining?

Data mining is the process of extracting knowledge or insights from large amounts of data using various statistical and computational techniques. The data can be structured, semi-structured or unstructured, and can be stored in various forms such as databases, data warehouses, and data lakes.

The primary goal of data mining is to discover hidden patterns and relationships in the data that can be used to make informed decisions or predictions. This involves exploring the data using various techniques such as clustering, classification, regression analysis, association rule mining, and anomaly detection.

Data mining has a wide range of applications across various industries, including marketing, finance, healthcare, and telecommunications. For example, in marketing, data mining can be used to identify customer segments and target marketing campaigns, while in healthcare, it can be used to identify risk factors for diseases and develop personalized treatment plans.

However, data mining also raises ethical and privacy concerns, particularly when it involves personal or sensitive data. It’s important to ensure that data mining is conducted ethically and with appropriate safeguards in place to protect the privacy of individuals and prevent misuse of their data.

Table of Content:

Introduction to Data Mining

Introduction to Data
What Kind of Information are we collecting?
Motivation Behind Data Mining
Data Mining Foundations
What is Data Mining?
Knowledge Discovery in Databases or KDD process
The Architecture of Data Mining
Different types of Data in Data Mining?
Aggregation
Data Mining Functionalities
Classification of Data Mining Systems
What are the issues in Data Mining?
Data Mining Tools
Data Mining in Science and Engineering
Data Mining for Intrusion Detection and Prevention
Data Mining for Financial Data Analysis
Data Mining for Retail and Telecommunication Industries

Data Preprocessing

Introduction to Data Preprocessing
Data Cleaning
Inconsistent Data
Data Integration
Data Transformation
Entity Identification Problem
Redundancy and Correlation Analysis
Tuple Duplication
Wavelet Transforms
Principal Components Analysis
Attribute Subset Selection
Numerosity Reduction
Bar Graphs and Histograms
Under Sampling and Over Sampling
Data Cube Aggregation
Discretization by Binning
Concept Hierarchy Generation
Discretization by Histogram Analysis
Discretization by Cluster
Feature extraction
Feature Transformation
Feature Selection

Concept Description, Mining Frequent Patterns, Associations, and Correlations

Data Generalization
Data Summarization
Analysis of attribute relevance
Mining Class Comparisons
Different measures of Dispersion?
Frequent item-set mining
Frequent pattern mining
Market Basket Analysis
Apriori Algorithm
Improving the Efficiency of Apriori
Frequent Pattern-Growth Algorithm
Mining Closed and Max Patterns
What are the various kind of association rules
Measuring the Quality of Association Rules
Pattern Evaluation Methods

Classification and Prediction

Preparing the data for classification and prediction
Comparing Classification and Prediction methods
Decision Tree Induction
Bayes Classification Methods
Rule-Based Classification

Classification: Advanced Methods

Bayesian Belief Networks
A Multilayer Feed-Forward Neural Network
Backpropagation in Data Mining
Associative Classification
Discriminative Frequent Pattern–Based Classification
Classification Using Frequent Patterns
k-Nearest-Neighbor Classifiers
Case-Based Reasoning
Genetic Algorithms
Rough Set Approach
Fuzzy Set Approaches
Multiclass Classification
Semi-Supervised Classification
Active Learning
Transfer Learning
Cluster Analysis
Partitioning Methods
Hierarchical Methods
Density-Based Methods
Grid-Based Methods
Probabilistic Model-Based Clustering
Clustering High-Dimensional Data
Clustering Graph and Network Data
Clustering with Constraints

Artificial Neural Network

Difference between ANN and BNN
Artificial Neural Networks and its Applications
Architecture of Neural Network
Use of Neural Networks in Data Mining
Advantages and Disadvantages of ANN

Outlier Detection

What Are Outliers?
Types of Outliers
Challenges of Outlier Detection
Proximity-Based Methods Clustering-Based Methods
Statistical Approaches
Distance-Based Outlier Detection and a Nested Loop Method
Clustering-Based Approaches
Classification-Based Approaches
Mining Collective Outliers
Outlier Detection in High-Dimensional Data
Finding Outliers in Subspaces

OLAP Technology

Introduction to OLAP
Motivations for using OLAP
Difference between OLAP and OLTP
Data Cube or OLAP Approach in Data Mining
OLAP Servers
OLAP Applications

Data Mining Trends and Research Frontiers

Mining Complex Data Types
Mining Sequence Data: Time-Series, Symbolic Sequences, and Biological Sequences
Mining Graphs and Networks
Mining Other Kinds of Data
Statistical Data Mining
Visual and Audio Data Mining
Ubiquitous and Invisible Data Mining
Privacy, Security, and Social Impacts of Data Mining

Introduction to Data Warehousing

What Is a Data Warehouse?
Differences between Operational Database Systems and Data Warehouses
History of Data Warehousing
Why do we need of Data Warehouse in data mining?
Why have separate Data warehouses?
Components or Building Blocks of Data Warehouse
Data Warehouse Tool
Components and Implementation for Data Warehouse
What is MetaData?
What is ETL Process in Data Warehouse
Dimensional Data Modeling
Multi-Dimensional Data Model
Data Mining Query Language
Measures: Their Categorization and Computation
Single-Layer Architectures
Two-Layer Architecture
Three-Layer Architecture
Data Warehouse Development Cycle Model
Rules for Data Warehouse Implementation

FAQs on Data Mining Tutorial

Q.1 how to learn about data mining.

Here the Step-by-Step Guide to learn about data Mining:- Learning about data mining requires a combination of theoretical knowledge and practical skills. Here are some steps you can take to learn about data mining: Learn the fundamentals: Start by learning the basics of statistics, probability, and linear algebra, as these are the foundations of data mining. You can take online courses or read textbooks to build a strong foundation in these areas. Learn data mining techniques: There are several data mining techniques, such as clustering, classification, regression analysis, association rule mining, and anomaly detection. Learn the theory and principles behind these techniques, as well as their applications in different domains. Choose a programming language: Data mining is heavily reliant on programming, so it’s important to choose a programming language to work with. Some popular languages for data mining include Python, R, and SQL. Learn how to use these languages to write code and implement data mining algorithms. Work on projects: Practice your data mining skills by working on real-world projects. This will help you gain hands-on experience in working with data and applying data mining techniques to solve problems. Take online courses and certifications: There are several online courses and certifications available that can help you learn about data mining. These courses often provide a structured learning path and offer hands-on experience with data mining tools and techniques. Join data mining communities: Join online communities and forums where you can connect with other data mining professionals and learn from their experiences. This can also help you stay up-to-date with the latest trends and technologies in the field. Attend conferences and workshops: Attend data mining conferences and workshops to network with other professionals and learn about the latest research and developments in the field.

Q.2 What are the three types of Data Mining?

The three types of data mining are: Descriptive data mining Predictive data mining Prescriptive data mining

Q.3 What are the four stages of Data Mining?

The four Stages of Data Mining Include:- Data Acquisition Data Cleaning, Preparation, and Transformation Data analysis, Modelling, Classification, and Forecasting Reports

Q.4 What are Data Mining Tools?

The Most Popular Data Mining tools that are used frequently nowadays are R, Python, KNIME, RapidMiner, SAS, IBM SPSS Modeler and Weka.

Q.5 Where i can Prepare Data Mining Interview?

Preparing for a data mining interview requires a combination of theoretical knowledge and practical skills. Here are some resources where you can prepare for a data mining interview: Online courses: Online courses are a great way to learn about data mining and prepare for an interview. Platforms such as Coursera, edX, and Udemy offer several courses on data mining that cover various topics, from the basics of data mining to advanced techniques. Textbooks: There are several textbooks on data mining that cover different topics and provide practical examples. Some popular books on data mining include “Data Mining: Concepts and Techniques” by Jiawei Han and Micheline Kamber and “Introduction to Data Mining” by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. Practice problems: Practice problems can help you prepare for an interview by testing your knowledge and skills. Websites such as Kaggle and HackerRank offer practice problems and challenges that cover various topics in data mining. Mock interviews: Mock interviews can help you prepare for an interview by simulating the interview experience. You can ask a friend or colleague to conduct a mock interview and provide feedback on your answers and presentation. Online forums and communities: Online forums and communities such as Quora, Reddit, and Stack Exchange can provide insights into common interview questions and offer tips and advice from other professionals in the field.

Please Login to comment...

Similar reads, improve your coding skills with practice.

What kind of Experience do you want to share?

Search Search Please fill out this field.

What Is Data Mining?

How It Works
Data Warehousing & Mining Software
The Process
Applications
Advantages and Disadvantages

Data Mining and Social Media

The bottom line.

Marketing Essentials

What Is Data Mining? How It Works, Benefits, Techniques, and Examples

Data mining is the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information.

Companies use data mining software to learn more about their customers. It can help them to develop more effective marketing strategies, increase sales, and decrease costs. Data mining relies on effective data collection , warehousing , and computer processing.

Key Takeaways

Data mining is the process of analyzing a large batch of information to discern trends and patterns.
Data mining can be used by corporations for everything from learning about what customers are interested in or want to buy to fraud detection and spam filtering.
Data mining programs break down patterns and connections in data based on what information users request or provide.
Social media companies use data mining techniques to commodify their users in order to generate profit.
This use of data mining has come under criticism as users are often unaware of the data mining happening with their personal information, especially when it is used to influence preferences.

Investopedia / Julie Bang

How Data Mining Works

Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It is used in credit risk management, fraud detection , and spam filtering. It also is a market research tool that helps reveal the sentiment or opinions of a given group of people. The data mining process breaks down into four steps:

Data is collected and loaded into data warehouses on site or on a cloud service.
Business analysts, management teams, and information technology professionals access the data and determine how they want to organize it.
Custom application software sorts and organizes the data.
The end user presents the data in an easy-to-share format, such as a graph or table.

Data Warehousing and Mining Software

Data mining programs analyze relationships and patterns in data based on user requests. It organizes information into classes.

For example, a restaurant may want to use data mining to determine which specials it should offer and on what days. The data can be organized into classes based on when customers visit and what they order .

In other cases, data miners find clusters of information based on logical relationships or look at associations and sequential patterns to draw conclusions about trends in consumer behavior.

Warehousing is an important aspect of data mining. Warehousing is the centralization of an organization's data into one database or program. It allows the organization to spin off segments of data for specific users to analyze and use depending on their needs.

Cloud data warehouse solutions use the space and power of a cloud provider to store data. This allows smaller companies to leverage digital solutions for storage, security, and analytics.

Data Mining Techniques

Data mining uses algorithms and various other techniques to convert large collections of data into useful output. The most popular types of data mining techniques include association rules, classification, clustering, decision trees, K-Nearest Neighbor, neural networks, and predictive analysis.

Association rules , also referred to as market basket analysis, search for relationships between variables. This relationship in itself creates additional value within the data set as it strives to link pieces of data. For example, association rules would search a company's sales history to see which products are most commonly purchased together; with this information, stores can plan, promote, and forecast.
Classification uses predefined classes to assign to objects. These classes describe the characteristics of items or represent what the data points have in common with each other. This data mining technique allows the underlying data to be more neatly categorized and summarized across similar features or product lines.
Clustering is similar to classification. However, clustering identifies similarities between objects, then groups those items based on what makes them different from other items. While classification may result in groups such as "shampoo," "conditioner," "soap," and "toothpaste," clustering may identify groups such as "hair care" and "dental health."
Decision trees are used to classify or predict an outcome based on a set list of criteria or decisions. A decision tree is used to ask for the input of a series of cascading questions that sort the dataset based on the responses given. Sometimes depicted as a tree-like visual, a decision tree allows for specific direction and user input when drilling deeper into the data.
K-Nearest Neighbor (KNN) is an algorithm that classifies data based on its proximity to other data. The basis for KNN is rooted in the assumption that data points that are close to each other are more similar to each other than other bits of data. This non-parametric, supervised technique is used to predict the features of a group based on individual data points.
Neural networks process data through the use of nodes. These nodes are comprised of inputs, weights, and an output. Data is mapped through supervised learning, similar to how the human brain is interconnected. This model can be programmed to give threshold values to determine a model's accuracy.
Predictive analysis strives to leverage historical information to build graphical or mathematical models to forecast future outcomes. Overlapping with regression analysis , this technique aims to support an unknown figure in the future based on current data on hand.

The Data Mining Process

To be most effective, data analysts generally follow a certain flow of tasks along the data mining process. Without this structure, an analyst may encounter an issue in the middle of their analysis that could have easily been prevented had they prepared for it earlier. The data mining process is usually broken into the following steps.

Step 1: Understand the Business

Before any data is touched, extracted, cleaned, or analyzed, it is important to understand the underlying entity and the project at hand. What are the goals the company is trying to achieve by mining data? What is their current business situation? What are the findings of a SWOT analysis ? Before looking at any data, the mining process starts by understanding what will define success at the end of the process.

Step 2: Understand the Data

Once the business problem has been clearly defined, it's time to start thinking about data. This includes what sources are available, how they will be secured and stored, how the information will be gathered, and what the final outcome or analysis may look like. This step also includes determining the limits of the data, storage, security, and collection and assesses how these constraints will affect the data mining process.

Step 3: Prepare the Data

Data is gathered, uploaded, extracted, or calculated. It is then cleaned, standardized, scrubbed for outliers, assessed for mistakes, and checked for reasonableness. During this stage of data mining, the data may also be checked for size as an oversized collection of information may unnecessarily slow computations and analysis.

Step 4: Build the Model

With a clean data set in hand, it's time to crunch the numbers. Data scientists use the types of data mining above to search for relationships, trends, associations, or sequential patterns. The data may also be fed into predictive models to assess how previous bits of information may translate into future outcomes.

Step 5: Evaluate the Results

The data-centered aspect of data mining concludes by assessing the findings of the data model or models. The outcomes from the analysis may be aggregated, interpreted, and presented to decision-makers that have largely been excluded from the data mining process to this point. In this step, organizations can choose to make decisions based on the findings.

Step 6: Implement Change and Monitor

The data mining process concludes with management taking steps in response to the findings of the analysis. The company may decide the information was not strong enough or the findings were not relevant, or the company may strategically pivot based on findings. In either case, management reviews the ultimate impacts of the business and recreates future data mining loops by identifying new business problems or opportunities.

Different data mining processing models will have different steps, though the general process is usually pretty similar. For example, the Knowledge Discovery Databases model has nine steps, the CRISP-DM model has six steps, and the SEMMA process model has five steps.

Applications of Data Mining

In today's age of information, almost any department, industry, sector , or company can make use of data mining.

Data mining encourages smarter, more efficient use of capital to drive revenue growth. Consider the point-of-sale register at your favorite local coffee shop. For every sale, that coffeehouse collects the time a purchase was made and what products were sold. Using this information, the shop can strategically craft its product line.

Once the coffeehouse knows its ideal line-up, it's time to implement the changes. However, to make its marketing efforts more effective, the store can use data mining to understand where its clients see ads, what demographics to target, where to place digital ads, and what marketing strategies most resonate with customers. This includes aligning marketing campaigns , promotional offers, cross-sell offers, and programs to the findings of data mining.

Manufacturing

For companies that produce their own goods, data mining plays an integral part in analyzing how much each raw material costs, what materials are being used most efficiently, how time is spent along the manufacturing process, and what bottlenecks negatively impact the process. Data mining helps ensure the flow of goods is uninterrupted.

Fraud Detection

The heart of data mining is finding patterns, trends, and correlations that link data points together. Therefore, a company can use data mining to identify outliers or correlations that should not exist. For example, a company may analyze its cash flow and find a reoccurring transaction to an unknown account. If this is unexpected, the company may wish to investigate whether funds are being mismanaged.

Human Resources

Human resources departments often have a wide range of data available for processing including data on retention, promotions, salary ranges, company benefits, use of those benefits, and employee satisfaction surveys. Data mining can correlate this data to get a better understanding of why employees leave and what entices new hires.

Customer Service

Customer satisfaction may be caused (or destroyed) by many events or interactions. Imagine a company that ships goods. A customer may be dissatisfied with shipping times, shipping quality, or communications. The same customer may be frustrated with long telephone wait times or slow e-mail responses. Data mining gathers operational information about customer interactions and summarizes the findings to pinpoint weak points and highlight what the company is doing right.

Advantages and Disadvantages of Data Mining

It drives profitability and efficiency

It can be applied to any type of data and business problem

It can reveal hidden information and trends

It is complex

Results and benefits are not guaranteed

It can be expensive

Pros Explained

Profitability and efficiency : Data mining ensures a company is collecting and analyzing reliable data. It is often a more rigid, structured process that formally identifies a problem, gathers data related to the problem, and strives to formulate a solution. Therefore, data mining helps a business become more profitable , more efficient, or operationally stronger.
Wide applications : Data mining can look very different across applications, but the overall process can be used with almost any new or legacy application. Essentially any type of data can be gathered and analyzed, and almost every business problem that relies on qualifiable evidence can be tackled using data mining.
Hidden information and trends : The end goal of data mining is to take raw bits of information and determine if there is cohesion or correlation among the data. This benefit of data mining allows a company to create value with the information they have on hand that would otherwise not be overly apparent. Though data models can be complex, they can also yield fascinating results, unearth hidden trends, and suggest unique strategies.

Cons Explained

Complexity : The complexity of data mining is one of its greatest disadvantages. Data analytics often requires technical skill sets and certain software tools. Smaller companies may find this to be a barrier of entry too difficult to overcome.
No guarantees : Data mining doesn't always mean guaranteed results. A company may perform statistical analysis, make conclusions based on strong data, implement changes, and not reap any benefits. This may be due to inaccurate findings, market changes, model errors, or inappropriate data populations . Data mining can only guide decisions and not ensure outcomes.
High cost : There is also a cost component to data mining. Data tools may require costly subscriptions, and some data may be expensive to obtain. Security and privacy concerns can be pacified, though additional IT infrastructure may be costly as well. Data mining may also be most effective when using huge data sets; however, these data sets must be stored and require heavy computational power to analyze.

Even large companies or government agencies have challenges with data mining. Consider the FDA's white paper on data mining that outlines the challenges of bad information, duplicate data, underreporting, or overreporting.

One of the most lucrative applications of data mining has been undertaken by social media companies. Platforms like Facebook, TikTok, Instagram, and X (formerly Twitter) gather reams of data about their users based on their online activities.

That data can be used to make inferences about their preferences. Advertisers can target their messages to the people who appear to be most likely to respond positively.

Data mining on social media has become a big point of contention, with several investigative reports and exposés showing just how intrusive mining users' data can be. At the heart of the issue is that users may agree to the terms and conditions of the sites not realizing how their personal information is being collected or to whom their information is being sold.

Examples of Data Mining

Data mining can be used for good, or it can be used illicitly. Here is an example of both.

eBay and e-Commerce

eBay collects countless bits of information every day from sellers and buyers. The company uses data mining to attribute relationships between products, assess desired price ranges, analyze prior purchase patterns, and form product categories.

eBay outlines the recommendation process as:

Raw item metadata and user historical data are aggregated.
Scripts are run on a trained model to generate and predict the item and user.
A KNN search is performed.
The results are written to a database.
The real-time recommendation takes the user ID, calls the database results, and displays them to the user.

Facebook-Cambridge Analytica Scandal

A cautionary example of data mining is the Facebook-Cambridge Analytica data scandal. During the 2010s, the British consulting firm Cambridge Analytica Ltd. collected personal data from millions of Facebook users. This information was later analyzed for use in the 2016 presidential campaigns of Ted Cruz and Donald Trump. It is suspected that Cambridge Analytica interfered with other notable events such as the Brexit referendum.

In light of this inappropriate data mining and misuse of user data, Facebook agreed to pay $100 million for misleading investors about its uses of consumer data. The Securities and Exchange Commission claimed Facebook discovered the misuse in 2015 but did not correct its disclosures for more than two years.

What Are the Types of Data Mining?

There are two main types of data mining: predictive data mining and descriptive data mining. Predictive data mining extracts data that may be helpful in determining an outcome. Description data mining informs users of a given outcome.

How Is Data Mining Done?

Data mining relies on big data and advanced computing processes including machine learning and other forms of artificial intelligence (AI). The goal is to find patterns that can lead to inferences or predictions from large and unstructured data sets.

What Is Another Term for Data Mining?

Data mining also goes by the less-used term "knowledge discovery in data," or KDD.

Where Is Data Mining Used?

Data mining applications have been designed to take on just about any endeavor that relies on big data. Companies in the financial sector look for patterns in the markets. Governments try to identify potential security threats. Corporations, especially online and social media companies, use data mining to create profitable advertising and marketing campaigns that target specific sets of users.

Modern businesses have the ability to gather information on their customers, products, manufacturing lines, employees, and storefronts. These random pieces of information may not tell a story, but the use of data mining techniques, applications, and tools helps piece together information .

The ultimate goal of the data mining process is to compile data, analyze the results, and execute operational strategies based on data mining results.

Shafique, Umair, and Qaiser, Haseeb. " A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA) ." International Journal of Innovation and Scientific Research . vol. 12, no. 1, November 2014, pp. 217-222.

Food and Drug Administration. " Data Mining at FDA – White Paper ."

eBay. " Building a Deep Learning Based Retrieval System for Personalized Recommendations ."

Federal Trade Commission. " FTC Issues Opinion and Order Against Cambridge Analytica for Deceiving Consumers About Collection of Facebook Data, Compliance With EU-U.S. Privacy Shield ."

U.S. Security and Exchange Commission. " Facebook to Pay $100 Million for Misleading Investors About the Risks It Faced From Misuse of User Data ."

Terms of Service
Editorial Policy
Privacy Policy

Data Mining

--> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> -->

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout, books and references, instructor bio.

Prof. Pabitra Mitra

Course certificate.

DOWNLOAD APP

SWAYAM SUPPORT

Please choose the SWAYAM National Coordinator for support. * :