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Description of the book - Data Mining Methods And Techniques

This text is written at an introductory level and provides real world problem analysis and practical examples as well as data mining theory to enable students to apply the concepts of data mining outside the classroom. This mix of theory and practical application makes this text directly suitable for a wide variety of undergraduate disciplines such as business computer science, students engineering, economics, statistics, marketing, information systems and information technology Features: The authors of this text have made a conscious effort to introduce the maths and stats aspects of data mining in a simple and clear manner to cater for students who don?t have a background in these areas. A free software product called Weka is available on the website for this text. Weka is a collection of machine learning algorithms for data mining tasks and contains tools for data pre-processing, classification, This text doesn't rely on students having knowledge of computer programming to understand data mining. Contains a good mix of theory and practical application and encourages students to problem solve in a real-world context. Table Of Contents: 1. Fundamentals of Data Mining2. Data Processing and Data Warehouse3. WEKA: An Attractive Data Mining Tool 4. The Classification Task 5. Artificial Neural Networks and Support Vector Machines 6. Model Evaluation Techniques 7. Association Rule Mining8. The Clustering Task9. The Estimation Task10. Mining of Time Series11. Text and Web Mining12. Privacy, Security and Ethical Issues in Data Mining

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