LEADER 04069cam 2200589 i 4500001 ocn862096372 003 OCoLC 005 20161027124509.0 008 141114s2015 njua b 001 0 eng 010 2014043340 019 861734428 020 9781118116197 (cloth) 020 1118116194 (cloth) 029 1 AU@ |b000053830362 029 1 NLGGC |b391068075 035 (Sirsi) o862096372 035 (OCoLC)862096372 |z(OCoLC)861734428 040 DLC |beng |erda |cDLC |dYDX |dBTCTA |dBDX |dYDXCP |dOCLCF |dCDX |dBEDGE |dZLM |dUtOrBLW 042 pcc 049 EREE 050 00 QA76.9.D343 |bL3776 2015 082 00 006.3/12 |223 100 1 Larose, Daniel T. |=^A872257 245 10 Data mining and predictive analytics / |cDaniel T. Larose, Chantal D. Larose. 250 Second edition. 264 1 Hoboken, New Jersey : |bJohn Wiley & Sons Inc., |c[2015] 300 xxix, 794 pages : |billustrations ; |c25 cm. 336 text |btxt |2rdacontent 337 unmediated |bn |2rdamedia 338 volume |bnc |2rdacarrier 490 1 Wiley series on methods and applications in data mining 504 Includes bibliographical references and index. 505 2 Part I. Data Preparation -- Chapter 1. An Introduction to Data Mining and Predictive Analytics -- Chapter 2. Data Preprocessing -- Chapter 3. Exploratory Data Analysis -- Chapter 4. Dimension-Reduction Methods -- Part II Statistical Analysis -- Chapter 5 Univariate Statistical Analysis -- Chapter 6. Multivariate Statistics -- Chapter 7. Preparing to Model the data -- Chapter 8. Simple Linear Regression -- Chapter 9. Multiple Regression and Model Building -- Part III. Classification -- Chapter 10. k-Nearest Neighbor Algorithm -- Chapter 11. Decision trees -- Chapter 12. Neural Networks -- Chapter 13. Logistic Regression -- Chapter 14. Naïve Bayes and Bayesian Networks -- Chapter 15. Model Evaluation Techniques -- Chapter 16. Cost-Benefit Analysis Using Data-Driven Costs -- Chapter 17. Cost-Benefit Analysis For Trinary and k-Nary Classification Models -- Chapter 18. Graphical Evaluation of Classification Models -- Part IV. Clustering -- Chapter 19. Hierarchical and k-Means Clustering -- Chapter 20. Kohonen Networks --Chapter 21. Birch Clustering-- Chapter 22. Measuring Cluster Goodness -- Part V. Association Rules -- Chapter 23. Association Rules -- Part VI. Enhancing Model Performance -- Chapter 24. Segmentation Models -- Chapter 25. Ensemble Methods: Bagging and Boosting -- Chapter 26. Model Voting and Propensity Averaging -- Part VII. Further Topics -- Chapter 27. Genetic Algorithms -- Chapter 28. Imputation of Missing Data -- Part VIII. Case Study: Predicting Response to Direct-Mail Marketing --Chapter 29. Case Study, Part 1: Business Understanding, Data Preparation, and Eda -- Chapter 30. Case Study, Part 2: Clustering and Principal Components Analysis -- Chapter 31. Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability -- Chapter 32. Case Study, Part 4: Modeling And Evaluation For High Performance Only. 650 0 Data mining. |=^A408868 650 0 Prediction theory. |=^A6714 650 7 Manuels. |2eclas 650 7 Fouille de données. |2eclas 650 7 Méthodes statistiques. |2eclas 650 7 Etudes de cas. |2eclas 650 7 Data mining. |2fast |0(OCoLC)fst00887946 650 7 Prediction theory. |2fast |0(OCoLC)fst01075037 700 1 Larose, Chantal D. |=^A1300384 830 0 Wiley series on methods and applications in data mining. |=^A1153685 938 Brodart |bBROD |n107659824 938 Baker and Taylor |bBTCP |nBK0013968044 938 Coutts Information Services |bCOUT |n26441308 938 YBP Library Services |bYANK |n11256420 949 QA76.9.D343 L3776 2015 |hJOYNER48 |ojssb |i30372014721739 994 C0 |bERE 596 1 998 3816144