| About The Book Insight Into Data Mining
Data Mining is an emerging technology that has made its way into science, engineering, commerce and industry as many existing inference methods are obsolete for dealing with massive datasets that get accumulated in data warehouses.
This comprehensive and up-to-date text aims at providing the reader with sufficient information about data mining methods and algorithms so that they can make use of these methods for solving real-world problems. The authors have taken care to include most of the widely used methods in data mining with simple examples so as to make the text ideal for classroom learning. To make the theory more comprehensible to the students, many illustrations have been used, and this in turn explains how certain parameters of interest change as the algorithm proceeds.
Designed as a textbook for the undergraduate and postgraduate students of computer science, information technology, and master of computer applications, the book can also be used for MBA courses in Data Mining in Business, Business Intelligence, Marketing Research, and Health Care Management. Students of Bioinformatics will also find the text extremely useful.
CD-ROM INCLUDED: The accompanying CD contains
• Large collection of datasets.
• Animation on how to use WEKA and ExcelMiner to do data mining.
• Thorough exposition to classical and modern clustering algorithms with illustrative examples.
• Lucid introduction to support vector machine algorithms with step-by-step implementation details of algorithms.
• Separate chapters on practical datasets and the results of mining, and usage of softwares like WEKA, ExcelMiner and GCLUTO.
• Indepth coverage of data preprocessing with examples.
• Description of all the main classical decision-tree algorithms such as 1D3, C.4.5, CHAID and CART with examples
Table of Contents:
1. Data Mining.
2. Data Mining from a Business Perspective.
3. Data Types, Input and Output of Data Mining Algorithms.
4. Decision Trees-Classification and Regression Trees.
5. Preprocessing and Postprocessing in Data Mining.
7. Association Rule Mining.
8. Machine Learning with Open Source and Commercial Software.
9. Algorithms for Classification and Regression.
10. Support Vector Machines.
11. Cluster Analysis.
12. Visualization of Multidimensional Data.