This document discusses data mining, including its motivations, process, techniques, business applications, and current issues. Data mining involves extracting knowledge from large amounts of data through techniques like classification, regression, and clustering. It has various business applications in marketing, finance, and insurance. Selecting the right application requires considering both technical factors like data quality and non-technical factors like potential benefits and management support.

1. Data Mining
• Motivation
• Synonym
• Process of DM
• Operation of DM
• DM techniques
• Business Application
• Application Selection
• Current Issues

2. Motivations for Data Mining
• Raw data rarely generates direct
benefits
• Its real value is realized when we
extract information and knowledge
useful
• Some queries are difficult to generate
with SQL
– Which records indicate fraud?
– Which customers are likely to buy product
A?

3. Motivations for DM
• Only 5%-10% of the collected data has
been ever analyzed to support the
decision-making process
• The amount of the data collected in an
organization continues to increase,
while our ability to analyze that data has
not kept up proportionately

4. Data Mining (DM)
• A technique which extracts knowledge
from massive data
• It is also known as Knowledge
Discovery and Databases (KDD)
– KDD is defined as the overall process
necessary to discover knowledge, while
DM is one particular activity which applies
a specific algorithm to extract knowledge
– However, these two terms are often used
interchangeably

5. Process of Data Mining
• Extracting knowledge from databases is
a five-step process
• The five-step process of knowledge
discovery is an interactive, iterative
process through which discovery is
evolved

6. Process of Data Mining
• Selecting Application Domain
• Selecting Target Data
• Preprocessing Data
• Extracting Information/Knowledge
• Interpretation and Evaluation

7. Operations of DM
• Classification
• Regression
• Link Analysis
• Segmentation
• Detecting Deviations

8. DM Techniques
• Machine Learning
– Induction
– Conceptual Clustering
• ANN
• Statistical Techniques
• Example-based Methods

9. Business Application
• Marketing
– Market Segmentation
– Market Basket Analysis
– Trend Analysis
– Sales Prediction
• Finance
– Bankruptcy prediction
– Credit approval

10. Business Application
• Finance
– Bond rate prediction
– Mutual fund selection
• Insurance
– Fraud Detection

11. Application Selection
• Non-Technical Criteria
– Potential benefits and payoffs
– Management support
– Domain expert
– End user interest and involvement
– Potential for privacy/legal issues

12. Application Selection
• Technical Criteria
– Sufficient amount of data
– High quality data
– Prior Knowledge

13. Current Issues
• Integration
– DM with OLAP
• Limited power of commercial DM Tools
• Data quality problem
• Multimedia data: video, audio, images,
etc.
• Scaling-up problem