06595cam a2200817 i 4500 ocn865297935 OCoLC 20171105084656.0 m o d cr ||||||||||| 131212s2014 enk ob 001 0 eng 2013049413 9781118763377 (electronic bk.) 1118763378 (electronic bk.) 9781118763728 (electronic bk.) 1118763726 (electronic bk.) 9781118763704 (electronic bk.) 111876370X (electronic bk.) 1306550696 9781306550697 1119977134 9781119977131 CHBIS 010259582 CHNEW 000720389 CHVBK 325944199 DEBBG BV042682891 DEBSZ 423170929 DEBSZ 446580929 NZ1 15548672 NZ1 15626494 NZ1 15920455 DEBBG BV043625852 AU@ 000052331730 DEBBG BV043396403 (OCoLC)865297935 (OCoLC)905245061 (OCoLC)929527020 (OCoLC)959867764 (OCoLC)961583802 (OCoLC)962668632 (OCoLC)966372719 CL0500000570 Safari Books Online DLC eng rda pn DLC YDX N$T IDEBK E7B CUS DG1 YDXCP CDX OCLCF STF B24X7 COO DEBSZ UMI OCLCQ OCLCO DEBBG OCLCQ OCLCO EBLCP OCLCO N$T OCLCO pcc MAIN HF5415.125 COM 000000 bisacsh 006.3/12 23 Ahlemeyer-Stubbe, Andrea. A practical guide to data mining for business and industry / Andrea Ahlemeyer-Stubbe, Shirley Coleman. [electronic resource] Chichester, West Sussex, United Kingdom : John Wiley & Sons Inc., 2014. 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Title page; Copyright page; Glossary of terms; Part I: Data mining concept; 1 Introduction; 1.1 Aims of the Book; 1.2 Data Mining Context; 1.3 Global Appeal; 1.4 Example Datasets Used in This Book; 1.5 Recipe Structure; 1.6 Further Reading and Resources; 2 Data mining definition; 2.1 Types of Data Mining Questions; 2.2 Data Mining Process; 2.3 Business Task: Clarification of the Business Question behind the Problem; 2.4 Data: Provision and Processing of the Required Data; 2.5 Modelling: Analysis of the Data; 2.6 Evaluation and Validation during the Analysis Stage 2.7 Application of Data Mining Results and Learning from the ExperiencePart II: Data mining Practicalities; 3 All about data; 3.1 Some Basics; 3.2 Data Partition: Random Samples for Training, Testing and Validation; 3.3 Types of Business Information Systems; 3.4 Data Warehouses; 3.5 Three Components of a Data Warehouse: DBMS, DB and DBCS; 3.6 Data Marts; 3.7 A Typical Example from the Online Marketing Area; 3.8 Unique Data Marts; 3.9 Data Mart: Do's and Don'ts; 4 Data Preparation; 4.1 Necessity of Data Preparation; 4.2 From Small and Long to Short and Wide; 4.3 Transformation of Variables 4.4 Missing Data and Imputation Strategies4.5 Outliers; 4.6 Dealing with the Vagaries of Data; 4.7 Adjusting the Data Distributions; 4.8 Binning; 4.9 Timing Considerations; 4.10 Operational Issues; 5 Analytics; 5.1 Introduction; 5.2 Basis of Statistical Tests; 5.3 Sampling; 5.4 Basic Statistics for Pre-analytics; 5.5 Feature Selection/Reduction of Variables; 5.6 Time Series Analysis; 6 Methods; 6.1 Methods Overview; 6.2 Supervised Learning; 6.3 Multiple Linear Regression for Use When Target is Continuous; 6.4 Regression When the Target is Not Continuous; 6.5 Decision Trees 6.6 Neural Networks6.7 Which Method Produces the Best Model? A Comparison of Regression, Decision Trees and Neural Networks; 6.8 Unsupervised Learning; 6.9 Cluster Analysis; 6.10 Kohonen Networks and Self-Organising Maps; 6.11 Group Purchase Methods: Association and Sequence Analysis; 7 Validation and Application; 7.1 Introduction to Methods for Validation; 7.2 Lift and Gain Charts; 7.3 Model Stability; 7.4 Sensitivity Analysis; 7.5 Threshold Analytics and Confusion Matrix; 7.6 ROC Curves; 7.7 Cross-Validation and Robustness; 7.8 Model Complexity; Part III: Data mining in action; 8 Marketing 8.1 Recipe 1: Response Optimisation: To Find and Address the Right Number of Customers8.2 Recipe 2: To Find the x% of Customers with the Highest Affinity to an Offer; 8.3 Recipe 3: To Find the Right Number of Customers to Ignore; 8.4 Recipe 4: To Find the x% of Customers with the Lowest Affinity to an Offer; 8.5 Recipe 5: To Find the x% of Customers with the Highest Affinity to Buy; 8.6 Recipe 6: To Find the x% of Customers with the Lowest Affinity to Buy; 8.7 Recipe 7: To Find the x% of Customers with the Highest Affinity to a Single Purchase "Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest."-- Unedited summary from book. Print version record and CIP data provided by publisher. Data mining. Marketing Data processing. Management Mathematical models. COMPUTERS General. bisacsh Data mining. fast (OCoLC)fst00887946 Management Mathematical models. fast (OCoLC)fst01007201 Marketing Data processing. fast (OCoLC)fst01010187 Marketing - Data processing. Electronic books. Electronic books. Print version: Ahlemeyer-Stubbe, Andrea. Practical guide to data mining for business and industry. Chichester, West Sussex, United Kingdom : Wiley, 2014 9781119977131 (DLC) 2013047218 (OCoLC)865179974 http://onlinelibrary.wiley.com/book/10.1002/9781118763704 Wiley Online Library ddc BK 207137 207137