000			01545nam a2200217 4500
008			170328b xxu||||| |||| 00| 0 eng d
020			_a9781118877432
082			_a006.312 _bSHM
100			_aShmueli, Galit _910753
245			_aData Mining for Business Analytics: Concepts, Techniques and Applications with JMP Pro
260			_bJohn Wiley & Sons, Inc. _c2017 _aNew Jersey
300			_a442p
500			_aPart I: Preliminaries 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 (k-NN) 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. Cluster Analysis Part VI: Forecasting Time Series 15. Handling Time Series 16. Regression-Based Forecasting 17. Smoothing Methods Part VII: Cases 18. Cases
600			_aData Mining _921042
600			_aBusiness Mathematics - Computer Programs _921203
600			_aData Processing _921204
700			_aBruce, Peter C. _921044
700			_aStephens, Mia L. _921045
700			_aPatel, Nitin R. _921046
942			_2ddc _cLB _k006.312 _mSHM
999			_c106533 _d106533