This document provides an introduction to data mining. It defines data mining as the process of analyzing large amounts of data from different perspectives to discover meaningful patterns and extract useful information. It discusses why data mining is needed due to issues like data explosion. It also outlines some common data mining techniques like classification, clustering, and association analysis. Additionally, it provides examples of applications of data mining in various industries and discusses advantages like improved marketing and benefits to governments while also acknowledging disadvantages around privacy, security, and potential misuse of information.

1. Data Mining
-BY
Patil Sneha
Thorat Ragini
-From Government Polytechnic
Mumbai, Bandra (E)

2. INTRODUCTION
 Why Data Mining?
 What is Data Mining?
 Data Mining – On What Kind of Data?
 Data Mining Techniques
 Applications Of Data Mining
 Advantages & Disadvantages of Data Mining
 Conclusion & References

3. Why Data Mining?
 Data Explosion Problem
• Automated Data Collection tools & matured
database technology lead to tremendous amount
of data stored in database, data warehouses &
other information repositories
 Lots Of Data is Being collected &
warehoused
• Web Data ,E Commerce
• Purchases Of Departments
• Bank/Credit Transactions

5. What is Data Mining?
 Data Mining Is Process of analysing data
from different perspectives & summarizing
into useful information
 Exploration & analysis, by automatic or semi
automatic means of large quantities of data in
order to discover meaningful patterns
 Extraction of implicit, previously unknown &
potentially useful information of data

6. Data Mining – On What Kind of
Data?
 Data Warehouses
 Relational database
 Transactional database
 Advanced DB & information repositories
• Object oriented and object relational databases
• Spatial Databases
• Time Series Data & Temporal data
• Text Databases & multimedia databases
• WWW(World Wide Web)

7. Data Mining Techniques
 Classification And Prediction
 Eg. Focused hiring
 Cluster Analysis
 Eg. Market Segmentation
 Outlier Analysis
 Eg. Fraud Detection
 Association Analysis
 Eg. Market Basket Analysis
 Evolution Analysis
 Eg. Forecasting Stock market index using Time series
analysis

8. Applications Of Data Mining
Industry Applications
Finance- Credit card Analysis
Insurance- Claims, Fraud Analysis
Telecommunication- Call Record Analysis
Transport- Logistics Management
Consumer Goods- Promotion Analysis
Scientific Research- Image, Video, Speech
Utilities- Power Usage Analysis

9. Advantages & Disadvantages of
Data Mining
 Advantages of Data Mining
 Marketing/Retail
 Finance/Banking
 Manufacturing
 Governments
 Disadvantages of Data Mining
 Privacy Issues
 Security Issues
 Misuse of Information/Inaccurate Information

10. Conclusion & References
 Conclusion
 Data mining brings a lot of benefits to business,
society, governments as well as individual.
 However privacy, security, and misuse of informa