Introduction to Data Mining and its Applications

Contents

1 Introduction to Data Mining Principles

1.1 Data Mining and Knowledge Discovery

1.2 Data Warehousing and Data Mining - Overview

1.3 Summary

1.4 Review Questions

2 Data Warehousing, Data Mining, and OLAP

2.1 Data Mining Research Opportunities and Challenges

2.2 Evolving Data Mining into Solutions for Insights

2.3 Knowledge Extraction Through Data Mining

2.4 Data Warehousing and OLAP

2.5 Data Mining and OLAP

2.6 Summary

2.7 Review Questions

3 Data Marts and Data Warehouse: Information Architecture for the Millennium

3.1 Data Marts, Data Warehouse, and OLAP

3.2 Data Warehousing for Healthcare: The Greatest Weapon in your Competitive Arsenal

3.3 Data Warehousing in the Telecommunications Industry

3.4 The Telecommunications Lifecycle

3.5 Security Issues in Data Warehouse

3.6 Data Warehousing: To Buy or To Build a Fundamental Choice for Insurers

3.7 Summary

3.8 Review Questions

4 Evolution and Scaling of Data Mining Algorithms

4.1 Data-Driven Evolution of Data Mining Algorithms

4.2 Scaling Mining Algorithms to Large DataBases

4.3 Summary

4.4 Review Questions

5 Emerging Trends and Applications of Data Mining

5.1 Emerging Trends in Business Analytics

5.2 Business Applications of Data Mining

5.3 Emerging Scienti.c Applications in Data Mining

5.4 Summary

5.5 Review Questions

6 Data Mining Trends and Knowledge Discovery

6.1 Getting a Handle on the Problem

6.2 KDD and Data Mining: Background

6.3 Related Fields

6.4 Summary

6.5 Review Questions

7 Data Mining Tasks, Techniques, and Applications

7.1 Reality Check for Data Mining

7.2 Data Mining: Tasks, Techniques, and Applications

7.3 Summary

7.4 Review Questions

8 Data Mining: an Introduction – Case Study

8.1 The Data Flood

8.2 Data Holds Knowledge

8.3 Data Mining: A New Approach to Information Overload

8.4 Summary

8.5 Review Questions

9 Data Mining & KDD

9.1 Data Mining and KDD – Overview

9.2 Data Mining: The Two Cultures

9.3 Summary

9.4 Review Questions

10 Statistical Themes and Lessons for Data Mining

10.1 Data Mining and O.cial Statistics

10.2 Statistical Themes and Lessons for Data Mining

10.3 Summary

10.4 Review Questions

11 Theoretical Frameworks for Data Mining

11.1 Two Simple Approaches

11.2 Microeconomic View of Data Mining

11.3 Inductive Databases

11.4 Summary

11.5 Review Questions

12 Major and Privacy Issues in Data Mining and Knowledge Discovery

12.1 Major Issues in Data Mining

12.2 Privacy Issues in Knowledge Discovery and Data Mining

12.3 Some Privacy Issues in Knowledge Discovery: The OECD Personal Privacy Guidelines

12.4 Summary

12.5 Review Questions

13 Active Data Mining

13.1 Shape De.nitions

13.2 Queries

13.3 Triggers

13.4 Summary

13.5 Review Questions

14 Decomposition in Data Mining - A Case Study

14.1 Decomposition in the Literature

14.2 Typology of Decomposition in Data Mining

14.3 Hybrid Models

14.4 Knowledge Structuring

14.5 Rule-Structuring Model

14.6 Decision Tables, Maps, and Atlases

14.7 Summary

14.8 Review Questions

15 Data Mining System Products and Research Prototypes

15.1 How to Choose a Data Mining System

15.2 Examples of Commercial Data Mining Systems

15.3 Summary

15.4 Review Questions

16 Data Mining in Customer Value and Customer Relationship Management

16.1 Data Mining: A Concept of Customer Relationship Marketing

16.2 Introduction to Customer Acquisition

16.3 Customer Relationship Management (CRM)

16.4 Data Mining and Customer Value and Relationships

16.5 CRM: Technologies and Applications

16.6 Data Management in Analytical Customer Relationship Management

16.7 Summary

16.8 Review Questions

17 Data Mining in Business

17.1 Business Focus on Data Engineering

17.2 Data Mining for Business Problems

17.3 Data Mining and Business Intelligence

17.4 Data Mining in Business - Case Studies

18 Data Mining in Sales Marketing and Finance

18.1 Data Mining can Bring Pinpoint Accuracy to Sales

18.2 From Data Mining to Database Marketing

18.3 Data Mining for Marketing Decisions

18.4 Increasing Customer Value by Integrating Data Mining and Campaign Management Software

18.5 Completing a Solution for Market-Basket Analysis – Case Study

18.6 Data Mining in Finance

18.7 Data Mining for Financial Data Analysis

18.8 Summary

18.9 Review Questions

19 Banking and Commercial Applications

19.1 Bringing Data Mining to the Forefront of Business Intelligence in Wholesale Banking

19.2 Distributed Data Mining Through a Centralized Solution – A Case Study

19.3 Data Mining in Commercial Applications

19.4 Decision Support Systems – Case Study

19.5 Keys to the Commercial Success of Data Mining – Case Studies

19.6 Data Mining Supports E-Commerce

19.7 Data Mining for the Retail Industry

19.8 Business Intelligence and Retailing

19.9 Summary

19.10 Review Questions

20 Data Mining for Insurance

20.1 Insurance Underwriting: Data Mining as an Underwriting Decision Support Systems

20.2 Business Intelligence and Insurance – Application of Business Intelligence Tools like Data Warehousing, OLAP and Data Mining in Insurance

20.3 Summary

20.4 Review Questions

21 Data Mining in Biomedicine and Science

21.1 Applications in Medicine

21.2 Data Mining for Biomedical and DNA Data Analysis

21.3 An Unsupervised Neural Network Approach to Medical Data Mining Techniques: Case Study

21.4 Data Mining – Assisted Decision Support for Fever Diagnosis – Case Study

21.5 Data Mining and Science

21.6 Knowledge Discovery in Science as Opposed to Business-Case Study

21.7 Data Mining in a Scienti.c Environment

21.8 Flexible Earth Science Data Mining System Architecture

21.9 Summary

21.10 Review Questions

22 Text and Web Mining

22.1 Data Mining and the Web

22.2 An Overview on Web Mining

22.3 Text Mining

22.4 Discovering Web Access Patterns and Trends

22.5 Web Usage Mining on Proxy Servers: A Case Study

22.6 Text Data Mining in Biomedical Literature

Approach – Case Study

22.7 Related Work

22.8 Summary

22.9 Review Questions

23 Data Mining in Information Analysis and Delivery

23.1 Information Analysis: Overview

23.2 Intelligent Information Delivery – Case Study

23.3 A Characterization of Data Mining Technologies and Processes – Case Study

23.4 Summary

23.5 Review Questions

24 Data Mining in Telecommunications and Control

24.1 Data Mining for the Telecommunication Industry

24.2 Data Mining Focus Areas in Telecommunication

24.3 A Learning System for Decision Support in Telecommunications – Case Study

24.4 Knowledge Processing in Control Systems

24.5 Data Mining for Maintenance of Complex Systems – A Case Study

24.6 Summary

24.7 Review Questions

25 Data Mining in Security

25.1 Data Mining in Security Systems

25.2 Real Time Data Mining-Based Intrusion Detection Systems – Case Study

25.3 Summary

Review Questions

APPENDIX-I Data Mining Research Projects

A.1 National University of Singapore: Data Mining Research Projects

A.2 HP Labs Research: Software Technology Laboratory

A.3 CRISP-DM: An Overview

A.4 Data Mining SuiteTM

A.5 The Quest Data Mining System, IBM Almaden Research Center, CA, USA

A.6 The Australian National University Research Projects

A.7 Data Mining Research Group, Monash University Australia

A.8 Current Projects, University of Alabama in Huntsville, AL

A.9 Kensington Approach Toward Enterprise Data Mining

APPENDIX-II Data Mining Standards

II.1 Data Mining Standards

II.2 Developing Data Mining Application Using Data Mining Standards

II.3 Analysis

II.4 Application Examples

II.5 Conclusion

Appendix 3A Intelligent Miner

3A.1 Data Mining Process

3A.2 Interpreting the Results

3A.3 Overview of the Intelligent Miner Components

3A.4 Running Intelligent Miner Servers

3A.5 How the Intelligent Miner Creates Output Data

3A.6 Performing Common Tasks

3A.7 Understanding Basic Concepts

3A.8 Main Window Areas

3A.9 Conclusion

Appendix 3B Clementine

3B.1 Key Findings

3B.2 Background Information

3B.3 Product Availability

3B.4 Software Description

3B.5 Architecture

3B.6 Methodology

3B.7 Clementine Server

3B.8 How Clementine Server Improves Performance on Large Datasets

3B.9 Conclusion

Appendix 3C Crisp

3C.1 Hierarchical Breakdown

3C.2 Mapping Generic Models to Specialized Models

3C.3 The CRISP-DM Reference Model

3C.4 Data Understanding

3C.5 Data Preparation

3C.6 Modeling

3C.7 Evaluation

3C.8 Conclusion

Appendix 3D Mineset

3D.1 Introduction

3D.2 Architecture

3D.3 MineSet Tools for Data Mining Tasks

3D.4 About the Raw Data

3D.5 Analytical Algorithms

3D.6 Visualization

3D.7 KDD Process Management

3D.8 History

3D.9 Commercial Uses

3D.10 Conclusion

Appendix 3E Enterprise Miner

3E.1 Tools For Data Mining Process

3E.2 Why Enterprise Miner

3E.3 Product Overview

3E.4 SAS Enterprise Miner 5.2 Key Features

3E.5 Enterprise Miner Software

3E.6 Enterprise Miner Process for Data Mining

3E.7 Client/Server Capabilities

3E.8 Client/Server Requirements

3E.9 Conclusion

References