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Full Description
Divided in two sections, this book is organized in 15 chapters. The first section covers data warehousing concepts and the steps required in creating a data warehouse for a decision support system along with data warehouse implementation case study. The second section provides a comprehensive introduction to data mining and is designed to be accessible and useful to students, instructors, researchers and professionals. It includes data preprocessing, visualization, predictive modeling, association analysis, clustering, and anomaly detection. The goal is to present fundamental concepts and algorithms for each topic, thus providing reader with the necessary background for the application of data mining to the real problems.
Salient Features
Important concepts of data warehousing and data mining.
Solved numerical problems and case studies. The exercises have been provided at the end of every chapter.
Chapters are organized into the following sections: Objectives, Theory and examples, Summary and Solved Problems.
Summary at the end of the chapter
Long and short answer type questions at the end of each chapter
Tables and figures for better illustration
Solutions to numerical problems.
Contents
1. Evolution of Decision Support Systems & Data Warehousing
2. From Data to Information
3. Data Warehouse Architecture and OLAP Servers
4. Defining the Business Requirements
5. Data Warehouse Environment
6. Data Warehouse Design
7. Data Warehouse Schema
8. Case Studies
9. Introduction to Data Mining
10. Understanding Data and Data Preprocessing
11. Frequent Pattern Mining
12. Classification
13. Clustering
14. A Brief Overview of Outlier Detection Techniques
15. Introduction to Web, Temporal and Spatial Mining
