文档介绍：Data Mining: An Overview from a Database Perspective Tutorial Outline PAKDD Conference 1998
Jiawei Han
Simon Fraser University, Canada
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PAKDD’98 tutorial notes
1. On-Line Analytical Mining
2. Clustering Techniques
3. Spatial Data Mining
4. Visual Data Mining
5. Towards Parallel On-Line Analytical Mining
6. Data Mining: The Next Step
7. References
Outline
Motivation
Data Warehousing and OLAP: An Overview
Data Mining: A General Introduction
On-Line Analytical Mining (OLAM): An Integration of OLAP with Mining
DBMiner: A System which Performs OLAP Mining
Future Research on On-Line Analytical Mining
Motivation: “Necessity is the Mother of Invention”
Data warehousing: Integration of data from multiple sources into large warehouses and support of on-line analytical processing and business decision making.
Data mining (knowledge discovery in databases): Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases.
Necessity: Data explosion problem --- computerized data collection tools and mature database technology lead to tremendous amounts of data stored in databases.
We are drowning in data, but starving for knowledge!
Why Data Mining? Potential Applications
Marketing
Corporate Analysis
Fraud Detection
Other Applications
Marketing
Sales Analysis
associations between product sales
beer and diapers
Customer Profiling
data mining can tell you what types of customers buy what products
Identifying Customer Requirements
identify the best products for different customers
use prediction to find what factors will attract new customers
Corporate Analysis
Finances
cash flow analysis and prediction
Resources
summarize and compare the resources and spending
Competition
compare with other competitors by summarizing data to the same lev