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Data Mining: Introductory and Advanced Topics

Data Mining: Introductory and Advanced Topics, 1st edition

Data Mining: Introductory and Advanced Topics
  • Margaret H. Dunham

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Overview

Margaret Dunham offers the experienced data base professional or graduate level Computer Science student an introduction to the full spectrum of Data Mining concepts and algorithms. Using a database perspective throughout, Professor Dunham examines algorithms, data structures, data types, and complexity of algorithms and space. This text emphasizes the use of data mining concepts in real-world applications with large database components.

KEY FEATURES:
  • Covers advanced topics such as Web Mining and Spatial/Temporal mining
  • Includes succinct coverage of Data Warehousing, OLAP, Multidimensional Data, and Preprocessing
  • Provides case studies
  • Offers clearly written algorithms to better understand techniques
  • Includes a reference on how to use Prototypes and DM products

Table of contents

I. INTRODUCTION.

 1. Introduction.

 2. Related Concepts.

 3. Data Mining Techniques.

II. CORE TOPICS.

 4. Classification.

 5. Clustering.

 6. Association Rules.

III. ADVANCED TOPICS.

 7. Web Mining.

 8. Spatial Mining.

 9. Temporal Mining.

IV. APPENDIX.

10. Data Mining Products.

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Published by Pearson (February 1st 2020) - Copyright © 2003