TY  - BOOK
AU  - Shmueli, Galit
AU  - Bruce, Peter C.;Patel, Nitin R.
TI  - Data mining for business analytics: concepts, techniques, and applications with XLMiner 
SN  - 9781118729274
AV  - HF 5548.2 .S44 2016 
PY  - 2016///
CY  - Hoboken, New Jersey
PB  - John Wiley & Son, Inc.
KW  - COMPUTER FILE
N1  - Includes bibliographical references and index; Part 1. Preliminaries -- 1. Introduction -- 2. Overview of the data mining process -- Part 2. Data exploration and dimension reduction -- 3. Data visualization -- 4. Dimension reduction -- Part 3. Performance evaluation -- 5. Evaluating predictive performance -- Part 4. Prediction and classification methods -- 6. Multiple linear regression -- 7. k-Nearest-Neighbors (k-NN) -- 8. teh 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 5. Mining relationships among records -- 14. Association rules and collaborative filtering -- 15. Cluster analysis -- Part 6. Forecasting time series -- 16. Handling time series -- 17. Regression-based forecasting -- 18. Smoothing methods -- Part 7. Data analytics -- 19. Social network analytics -- 20. text mining -- Part 8. Cases -- 21. Cases
N2  - Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.  Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology
ER  -