Overview

This concise and approachable introduction to data mining selects 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.

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9781844808915

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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

Author Information

Hongbo Du is a lecturer in the Applied Computing Department, University of Buckingham, and specializes in database systems and data mining. He has been teaching data mining to undergraduate students and taught master students since 1997. He also designed and taught a similar course at City University London between 1998 and 2004, and more recently at Sarajevo School of Science and Technology. Author of a number of publications in the field, he has been involved with commercial data mining projects. He is also a visiting senior lecturer at Sarajevo School of Science and Technology.

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