Data Mining Techniques and Applications : An Introduction / Hongbo Du

It includes bibliography and index pages.

Overview:

'This concise and approachable introduction to data mining s a mixture of data mining techniques originating from statistics, machine learning and databases, and presents them in an algorithmic approach. Aimed primarily at undergraduate readers, it presents not only the fundamental principles and concepts of the subject in an easy-to-understand way, but also hands on, practical instruction on data mining techniques, that readers can put into practice as they go along using the freely downloadable Weka toolkit. Author Hongbo Du shares his years of commercial, as well as research-based, experience in the field through extensive examples and real-world case studies, highlighting how data mining solutions provided by software tools are used in practical problem solving. Covering not only traditional areas of data mining such as association, clustering and classification, this text also explains topics such as data warehousing, online-analytic processing, and text mining.

Features:

' Practical, hands-on instruction in data mining techniques using Weka Provides an easy-to-follow coverage of data mining algorithms via use of pseudo code and abstract data structures together with illustrative examples Real-life case study and examples demonstrating the practical applications of data mining techniques.

Table of Contents:

'1. Introduction 2. Principles of Data Mining 3. Data, Data Pre-processing and Data Exploration 4. Basic Techniques for Cluster Detection 5. Advanced Techniques for Cluster Detection 6. Decision Tree Induction Techniques for Classification 7. Other Techniques for Classification 8. Techniques for Boolean Association Rule Discovery 9. Techniques for Other Types of Association Rules 10. Data Mining in Practice.