LEADER 05440nam 2200685Ia 450 001 9910455538003321 005 20200520144314.0 010 $a1-282-16831-2 010 $a9786612168314 010 $a0-08-091203-6 035 $a(CKB)1000000000766628 035 $a(EBL)452830 035 $a(OCoLC)500575206 035 $a(SSID)ssj0000298268 035 $a(PQKBManifestationID)12098230 035 $a(PQKBTitleCode)TC0000298268 035 $a(PQKBWorkID)10343234 035 $a(PQKB)11677600 035 $a(MiAaPQ)EBC452830 035 $a(PPN)170601579 035 $a(Au-PeEL)EBL452830 035 $a(CaPaEBR)ebr10310724 035 $a(CaONFJC)MIL216831 035 $a(EXLCZ)991000000000766628 100 $a20090313d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHandbook of statistical analysis and data mining applications$b[electronic resource] /$fRobert Nisbet, John Elder, Gary Miner 210 $aAmsterdam ;$aBoston $cAcademic Press/Elsevier$dc2009 215 $a1 online resource (859 p.) 300 $aDescription based upon print version of record. 311 $a0-12-374765-1 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Handbook of Statistical Analysis and Data Mining Applications; Copyright Page; Table of Contents; Foreword 1; Foreword 2; Preface; Introduction; List of Tutorials by Guest Authors; Part 1: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process; Chapter 1: The Background for Data Mining Practice; Assumptions of the Parametric Model; Two Views of Reality; Aristotle; Plato; The Rise of Modern Statistical Analysis: The Second Generation; Machine Learning Methods: The Third Generation; Statistical Learning Theory: The Fourth Generation 327 $aChapter 2: Theoretical Considerations for Data MiningMajor Issues in Data Mining; General Requirements for Success in a Data Mining Project; The Importance of Domain Knowledge; Postscript; Some Caveats with Data Mining Solutions; Chapter 3: The Data Mining Process; CRISP-DM; Assess the Business Environment for Data Mining; Data Understanding (Mostly Science); References; Preamble; Chapter 4: Data Understanding and Preparation; Preamble; Issues That Should be Resolved; Splitting Data 327 $aPart 1: Using a Wrapper Approach in Weka to Determine the Most Appropriate Variables for Your Neural Network ModelExample 4; Data Extraction; Data Weighting and Balancing; Data Filtering and Smoothing; Data Abstraction; Data Reduction; Data Sampling; Data Discretization; Data Derivation; Postscript; Chapter 5: Feature Selection; Inductive Database Approach; Bi-variate Methods; Multivariate Methods; Postscript; Complex Methods; The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace; Preamble; Chapter 6: Accessory Tools for Doing Data Mining; Preamble; Introduction 327 $aBasic Descriptive StatisticsCombining Groups (Classes) for Predictive Data Mining; Generalized Linear Models (GLMs); Data Miner Workspace Templates; Comparison of Models with and Without Time-Based Features; Example: The IDP Facility of STATISTICA Data Miner; Ensembles in General; Part 2: The Algorithms in Data Mining and Text Mining, the Organization of the Three most common Data Mining Tools, and Selected Speci...; Chapter 7: Basic Algorithms for Data Mining: A Brief Overview; Preamble; STATISTICA Data Miner Recipe (DMRecipe); Automated Neural Nets; Generalized Additive Models (GAMs) 327 $aOutputs of GAMsRecursive Partitioning; Pruning Trees; Bibliography; Chapter 8: Advanced Algorithms for Data Mining; The Physical Data Mart; Summary; Micro-Target the Profitable Customers; Quality Control Data Mining and Root Cause Analysis; Chapter 9: Text Mining and Natural Language Processing; The Development of Text Mining; Chapter 10: The Three Most Common Data Mining Software Tools; Preamble; SPSS Clementine Overview; Preamble; Setting the Default Directory; Visual Data Preparation for Data Mining: Taking Photos, Moving Pictures, and Objects into Spreadsheets Representing the Photos... 327 $aPreamble 330 $a The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be a 606 $aData mining$xStatistical methods 606 $aMultivariate analysis 608 $aElectronic books. 615 0$aData mining$xStatistical methods. 615 0$aMultivariate analysis. 676 $a006.3/12 22 676 $a519.5 700 $aNisbet$b Robert$0142531 701 $aElder$b John F$g(John Fletcher)$0783974 701 $aMiner$b Gary$0322168 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455538003321 996 $aHandbook of statistical analysis and data mining applications$91741883 997 $aUNINA