A practical guide to data mining for business and industry /

"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 methodolog...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Ahlemeyer-Stubbe, Andrea
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Chichester, West Sussex, United Kingdom : John Wiley & Sons Inc., 2014.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 06612nam a2200817 4500
001 ocn865297935
003 OCoLC
005 20170124070606.8
006 m o d
007 cr |||||||||||
008 131212s2014 enk ob 001 0 eng
010 |a  2013049413 
040 |a DLC  |b eng  |e rda  |e pn  |c DLC  |d YDX  |d N$T  |d IDEBK  |d E7B  |d CUS  |d DG1  |d YDXCP  |d CDX  |d OCLCF  |d STF  |d B24X7  |d COO  |d DEBSZ  |d UMI  |d OCLCQ  |d OCLCO  |d DEBBG  |d OCLCQ  |d OCLCO  |d EBLCP  |d OCLCO  |d N$T  |d OCLCO  |d GrThAP 
019 |a 905245061  |a 929527020  |a 959867764  |a 961583802  |a 962668632  |a 966372719 
020 |a 9781118763377  |q (electronic bk.) 
020 |a 1118763378  |q (electronic bk.) 
020 |a 9781118763728  |q (electronic bk.) 
020 |a 1118763726  |q (electronic bk.) 
020 |a 9781118763704  |q (electronic bk.) 
020 |a 111876370X  |q (electronic bk.) 
020 |z 1306550696 
020 |z 9781306550697 
020 |z 1119977134 
020 |z 9781119977131 
029 1 |a CHBIS  |b 010259582 
029 1 |a CHNEW  |b 000720389 
029 1 |a CHVBK  |b 325944199 
029 1 |a DEBBG  |b BV042682891 
029 1 |a DEBSZ  |b 423170929 
029 1 |a DEBSZ  |b 446580929 
029 1 |a NZ1  |b 15548672 
029 1 |a NZ1  |b 15626494 
029 1 |a NZ1  |b 15920455 
029 1 |a DEBBG  |b BV043625852 
029 1 |a AU@  |b 000052331730 
029 1 |a DEBBG  |b BV043396403 
035 |a (OCoLC)865297935  |z (OCoLC)905245061  |z (OCoLC)929527020  |z (OCoLC)959867764  |z (OCoLC)961583802  |z (OCoLC)962668632  |z (OCoLC)966372719 
037 |a CL0500000570  |b Safari Books Online 
042 |a pcc 
050 0 0 |a HF5415.125 
072 7 |a COM  |x 000000  |2 bisacsh 
082 0 0 |a 006.3/12  |2 23 
049 |a MAIN 
100 1 |a Ahlemeyer-Stubbe, Andrea. 
245 1 2 |a A practical guide to data mining for business and industry /  |c Andrea Ahlemeyer-Stubbe, Shirley Coleman. 
264 1 |a Chichester, West Sussex, United Kingdom :  |b John Wiley & Sons Inc.,  |c 2014. 
300 |a 1 online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record and CIP data provided by publisher. 
520 |a "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."--  |c Unedited summary from book. 
505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
650 0 |a Data mining. 
650 0 |a Marketing  |x Data processing. 
650 0 |a Management  |x Mathematical models. 
650 7 |a COMPUTERS  |x General.  |2 bisacsh 
650 7 |a Data mining.  |2 fast  |0 (OCoLC)fst00887946 
650 7 |a Management  |x Mathematical models.  |2 fast  |0 (OCoLC)fst01007201 
650 7 |a Marketing  |x Data processing.  |2 fast  |0 (OCoLC)fst01010187 
650 4 |a Marketing - Data processing. 
655 4 |a Electronic books. 
655 0 |a Electronic books. 
776 0 8 |i Print version:  |a Ahlemeyer-Stubbe, Andrea.  |t Practical guide to data mining for business and industry.  |d Chichester, West Sussex, United Kingdom : Wiley, 2014  |z 9781119977131  |w (DLC) 2013047218  |w (OCoLC)865179974 
856 4 0 |u https://doi.org/10.1002/9781118763704  |z Full Text via HEAL-Link 
994 |a 92  |b DG1