1. Amarjit Kaur
Samiksha Sharma

2.  What is data mining ?
 Why data mining ?
 Data mining as a necessity
 Evolution of database
 Origin of data mining
 Data mining : A KDD process
 Applications
 Management areas
 Examples
 techniques

3.  Extration of implicit, previously unknown and
potentially useful information from data
 Exploration & analysis by automatic and semi
automatic means of large quantities of data
in order to discover meaningful patterns

4.  Lots of data is being collected and
warehoused
 Web data , e commerce
 Purchases at departmental store/ groceries
store
 Bank/credit card transactions
 Computers have become cheaper and more
powerful
 Competitve pressure is strong
 Provide better,customized services for an
edge (e.g in customer relationship
management)

5. DATA MINING –
AS A
NECESSITY

6. DATA EXPLOSION PROBLEM
 Automated data collection tools and mature
database technology lead to tremendous
amounts of data stored in databases, data
warehouses and other information
repositories
 We are drowning in data, but starving for
knowledge!
 Solution : data mining
 Extraction of interesting knowledge(rules,
regularities,patterns,constraints) from data
in large databases

7.  1960s:
Data collection, database creation, IMS and
network DBMS.
 1970s:
Relational data model, relational DBMS
implementation
 1980s:
RDBMS, advanced data models( extended
relational, OO, deductive sets) and application-
oriented DBMS(spatial,scientific,engineering etc)
 1990s-2000s:
Data mining and data warehousing, multimedia
databases, and web databases

8.  The term “data mining” was introduced in the
1990s. Data mining roots are traced back along
three family lines:
Classical
statistics
Artificial
intelligence
Machine
learning

9.  Statistical are the foundations of most technologies on
which data mining is built, e.g. regression analysis,
standard deviation etc. All these are used to study data
and data relationships.
 Artificial intelligence which is built upon heuristics as
opposed to statistics, attempts to apply human-
thoughts like processing to statistical problems. E.g.
RDBMS.
 Machine learning is to union of statistics and AI.
 DATA MINING therefore uses AI and statistical approach
together. It blends AI heuristics with advanced
statistical analysis to study data and find previously –
hidden trends or patterns within company using
statistical fundamental concepts and adding more
advanced AI algorithms to achieve the goal.

11.  Database analysis and decision support
 Market analysis and management
 Target marketing, customer relation
management, market basket analysis, cross
selling, market segmentation
 Risk analysis and management
 Forecasting, customer retention,improved
underwriting, quality control,competitive
analysis
 Fraud detection and management
 Other applications
 Text mining(news group,email,documents) and
web analysis
 Intelligent query answering

12.  Sports
 IBM Advanced Scout analyzed NBA game statistics to
gain competitive advantage for New York Knicks and
Miami Heat
 Astronomy
 JPL and the Palomar Observatory discovered 22
quasars with the help of data-mining.
 Internet Web Surf-Aid
 IBM Surf-Aid applies data mining algorithms to web
access logs for market-related pages to discover
customer preference and behaviour pages, analyzing
effectiveness of web marketing, improving web site
organizations etc

13.  Cross-market analysis
 Associations/Co-relations between product sales
 Prediction based on the associations information
 Customer profiling
 Data mining can tell you what types of customers
buy what products.
 Identifying customer requirements
 Identifying the best products for different
customers.
 Use prediction to find what factors will attract new
customers.
 Provides summary information
 Various multidimensional summary reports
 Statistical summary information

14.  Finance planning and asset evaluation
i. Cash flow analysis and prediction
ii. Contingent claim analysis to evaluate assets
iii. Cross-sectional and time series analysis
 Resources planning
i. Summarize and compare the resources and
spending
 Competition
i. Monitor competition and market directions
ii. Set price strategy in the market
iii. Grouping of customer into classes

15.  Applications
 Widely used in health care, retail, credit card
services, telecommunications etc.
 Approach
 Use historical data to build models of fraudulent
behaviour and use data mining to help identify
similar instances.
 Examples
 Auto insurance : detect a group of people who
stage accidents to collect on insurance.
 Money laundering : detect suspicious money
transactions

20. Techniques
CLASSIFICATION
ASSOCIATION
SEQUENCE
CLUSTER