Data Mining: Introductory and Advanced Topics

About

Features

• A database perspective is used throughout.

  • Provides students with a focused discussion of algorithms, data structures, data types, and complexity of algorithms and space.

• Clearly written algorithms.

  • Enables students to better understand techniques.

• An emphasis on the use of data mining concepts in real-world applications with large database components.

• Appendix providing overview of available data mining products.

• Strategic text organisation of four major sections: Introduction, Core Topics, Advanced Topics, and Products.

  • Enables instructors to choose portions of the book to cover in a limited period of time.

Description

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.

• 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

Sample Content

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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