Data Mining: A Tutorial-Based Primer, Second Edition (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Richard J. Roiger

  • 出版商: Chapman
  • 出版日期: 2016-12-01
  • 定價: $1,400
  • 售價: 8.5$1,190
  • 語言: 英文
  • 頁數: 529
  • 裝訂: Paperback
  • ISBN: 1498763979
  • ISBN-13: 9781498763974
  • 相關分類: Data-mining 資料探勘

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Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools.

 Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more.

 The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.

目錄大綱

Section I Data Mining Fundamentals
 1. Data Mining: A First View
 2. Data Mining: A Closer Look
 3. Basic Data Mining Techniques

Section II Tools for Knowledge Discovery
 4. Weka—An Environment for Knowledge Discovery
 5. Knowledge Discovery with RapidMiner
 6. The Knowledge Discovery Process
 7. Formal Evaluation Techniques

Section III Building Neural Networks
 8. Neural Networks
 9. Building Neural Networks with Weka
 10. Building Neural Networks with RapidMiner

Section IV Advanced Data Mining Techniques
 11. Supervised Statistical Techniques
 12. Unsupervised Clustering Techniques
 13. Specialized Techniques
 14. The Data Warehouse