The document discusses the distinction between data, information, and knowledge, emphasizing the role of data mining within business intelligence to uncover patterns from large datasets. It highlights challenges related to data access and understanding in the context of data overload and suggests solutions like data warehousing and analytical processing. Additionally, the document outlines various applications of data mining across industries, data mining techniques, and addresses concerns about privacy and security.

1. Making Sense Of Data
Data Mining
01

2. DATA, INFORMATION &
KNOWLEDGE
Data:
The most
elementary
descriptions of
things, events,
activities, and
transactions
02
Information:
Organized data
that has meaning
and value
Knowledge:
The concept of
understanding
information based
on recognized
patterns in a way
that provides
insight to
information.

3. Business
Intelligence
03
Business intelligence ( BI ) refers to applications and technologies
which are used to gather, provide access to, and analyze data and
information about their company operations.
Data Mining is an important part of Business Intelligence.

4. WHAT IS DATA
MINING?
The task of discovering interesting
patterns from large amounts of
data
04

5. Why Data Mining?
05
I can’t find the data I need
I can’t get the data I need
I can’t understand the data I found
I can’t use the data I found
Data, Data everywhere yet …

6. Why Data Mining?
06
Data explosion problem
Advance data collection tools and database
technology lead to tremendous amounts of data
stored in databases.
We are drowning in data, but starving for
knowledge!
Solution: Data warehousing and data mining
Data warehousing and on-line analytical
processing
Extraction of interesting knowledge using Data
Mining

7. APPLICATION OF DATA
MINING
Industry
Finance
Insurance
Telecommunication
Transport
Consumer goods Scientific
Research
07
Application
Credit Card Analysis
Claims,Fraud Analysis
Call record analysis
Logistics management
promotion analysis
Image, Video, Speech Utilities
Power usage analysis

8. STEPS OF DATA MINING
09

9. DATA MINING TECHNIQUES
10
Classification and Prediction
Cluster Analysis
Outlier Analysis
Association Analysis
Evolution Analysis

10. DATA MINING: ON WHAT
KINDS OF DATA?
11
Relational database
Data warehouse
Transactional database
Advanced database and information repository

11. IS DATA MINING A THREAT
TO PRIVACY AND
INFORMATION SECURITY?
12
Purpose Specifications & Use Limitation
Openness
Security Measures like encryption
Solutions:

12. Microsoft SQL Server 2005
Microsoft SQL Server 2008
Oracle Data Mining
DBMiner
DATA MINING
TOOLS
13

13. Thank You

14. Presented By:
Dev Adnani - IU1983830001