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Data mining for business analytics : concepts, techniques, and applications with XLMiner / Galit Shmueli, Peter C. Bruce and Nitin R. Patel.

By: Contributor(s): Material type: TextTextPublication details: New Jersey : John Wiley & Sons, ©2016.Edition: 3rd edDescription: xxxi, 514 pages : illustrations ; 26 cmISBN:
  • 9781118729274
Subject(s): Additional physical formats: Online version:: Data mining for business analyticsDDC classification:
  • 006.312  23 Sh558
Contents:
Part I: 1. Introduction -- 2. Overview of the data mining process -- Part II: Data Exploration and Dimension Reduction. 3. Data visualization -- 4. Dimension reduction: Part III: Performance Evaluation. 5. Evaluating predictive performance -- Part IV: Prediction and Classification Methods. 6. Multiple linear regression -- 7. k-Nearest Neighbors (kNN) -- 8. The naive bayes classifier -- 9. Classification and regression trees -- 10. Logistic regression -- 11. Neural nets -- 12. Discriminant analysis -- 13. Combining methods : ensembles and uplift modeling -- Part V: Mining Relationships Among Records. 14. Association rules and collaborative filtering -- 15. Cluster analysis -- Part VI: Forecasting Time Series. 16. Handling time series -- 17. Regression-based forecasting -- 18. Smoothing methods -- Part VII: Data Analytics. 19. Social network analytics -- 20. Text mining -- Part VII Cases : 21. Cases.
Summary: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
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Includes bibliographical references and index.

Part I:
1. Introduction --
2. Overview of the data mining process --
Part II: Data Exploration and Dimension Reduction.
3. Data visualization --
4. Dimension reduction:
Part III: Performance Evaluation.
5. Evaluating predictive performance --
Part IV: Prediction and Classification Methods.
6. Multiple linear regression --
7. k-Nearest Neighbors (kNN) --
8. The naive bayes classifier --
9. Classification and regression trees --
10. Logistic regression --
11. Neural nets --
12. Discriminant analysis --
13. Combining methods : ensembles and uplift modeling --
Part V: Mining Relationships Among Records.
14. Association rules and collaborative filtering --
15. Cluster analysis --
Part VI: Forecasting Time Series.
16. Handling time series --
17. Regression-based forecasting --
18. Smoothing methods --
Part VII: Data Analytics.
19. Social network analytics --
20. Text mining --
Part VII Cases :
21. Cases.

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.

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