LEADER 05445nam 2200673Ia 450 001 9910145958103321 005 20200520144314.0 010 $a1-282-03107-4 010 $a9786612031076 010 $a0-470-41740-4 010 $a0-470-41739-0 035 $a(CKB)1000000000719469 035 $a(EBL)427614 035 $a(SSID)ssj0000196085 035 $a(PQKBManifestationID)11178735 035 $a(PQKBTitleCode)TC0000196085 035 $a(PQKBWorkID)10142486 035 $a(PQKB)10854392 035 $a(Au-PeEL)EBL427614 035 $a(CaPaEBR)ebr10296703 035 $a(CaONFJC)MIL203107 035 $a(OCoLC)352829637 035 $a(CaSebORM)9780470222805 035 $a(MiAaPQ)EBC427614 035 $a(EXLCZ)991000000000719469 100 $a20080602d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMaking sense of data II$b[electronic resource] $ea practical guide to data visualization, advanced data mining methods, and applications /$fGlenn J. Myatt, Wayne P. Johnson 205 $a1st edition 210 $aHoboken, N.J. $cJohn Wiley & Sons$dc2009 215 $a1 online resource (307 p.) 300 $aDescription based upon print version of record. 311 $a0-470-22280-8 320 $aIncludes bibliographical references (p. 273-277) and index. 327 $aMAKING SENSE OF DATA II; CONTENTS; PREFACE; 1 INTRODUCTION; 1.1 Overview; 1.2 Definition; 1.3 Preparation; 1.3.1 Overview; 1.3.2 Accessing Tabular Data; 1.3.3 Accessing Unstructured Data; 1.3.4 Understanding the Variables and Observations; 1.3.5 Data Cleaning; 1.3.6 Transformation; 1.3.7 Variable Reduction; 1.3.8 Segmentation; 1.3.9 Preparing Data to Apply; 1.4 Analysis; 1.4.1 Data Mining Tasks; 1.4.2 Optimization; 1.4.3 Evaluation; 1.4.4 Model Forensics; 1.5 Deployment; 1.6 Outline of Book; 1.6.1 Overview; 1.6.2 Data Visualization; 1.6.3 Clustering; 1.6.4 Predictive Analytics 327 $a1.6.5 Applications1.6.6 Software; 1.7 Summary; 1.8 Further Reading; 2 DATA VISUALIZATION; 2.1 Overview; 2.2 Visualization Design Principles; 2.2.1 General Principles; 2.2.2 Graphics Design; 2.2.3 Anatomy of a Graph; 2.3 Tables; 2.3.1 Simple Tables; 2.3.2 Summary Tables; 2.3.3 Two-Way Contingency Tables; 2.3.4 Supertables; 2.4 Univariate Data Visualization; 2.4.1 Bar Chart; 2.4.2 Histograms; 2.4.3 Frequency Polygram; 2.4.4 Box Plots; 2.4.5 Dot Plot; 2.4.6 Stem-and-Leaf Plot; 2.4.7 Quantile Plot; 2.4.8 Quantile-Quantile Plot; 2.5 Bivariate Data Visualization; 2.5.1 Scatterplot 327 $a2.6 Multivariate Data Visualization2.6.1 Histogram Matrix; 2.6.2 Scatterplot Matrix; 2.6.3 Multiple Box Plot; 2.6.4 Trellis Plot; 2.7 Visualizing Groups; 2.7.1 Dendrograms; 2.7.2 Decision Trees; 2.7.3 Cluster Image Maps; 2.8 Dynamic Techniques; 2.8.1 Overview; 2.8.2 Data Brushing; 2.8.3 Nearness Selection; 2.8.4 Sorting and Rearranging; 2.8.5 Searching and Filtering; 2.9 Summary; 2.10 Further Reading; 3 CLUSTERING; 3.1 Overview; 3.2 Distance Measures; 3.2.1 Overview; 3.2.2 Numeric Distance Measures; 3.2.3 Binary Distance Measures; 3.2.4 Mixed Variables; 3.2.5 Other Measures 327 $a3.3 Agglomerative Hierarchical Clustering3.3.1 Overview; 3.3.2 Single Linkage; 3.3.3 Complete Linkage; 3.3.4 Average Linkage; 3.3.5 Other Methods; 3.3.6 Selecting Groups; 3.4 Partitioned-Based Clustering; 3.4.1 Overview; 3.4.2 k-Means; 3.4.3 Worked Example; 3.4.4 Miscellaneous Partitioned-Based Clustering; 3.5 Fuzzy Clustering; 3.5.1 Overview; 3.5.2 Fuzzy k-Means; 3.5.3 Worked Examples; 3.6 Summary; 3.7 Further Reading; 4 PREDICTIVE ANALYTICS; 4.1 Overview; 4.1.1 Predictive Modeling; 4.1.2 Testing Model Accuracy; 4.1.3 Evaluating Regression Models' Predictive Accuracy 327 $a4.1.4 Evaluating Classification Models' Predictive Accuracy4.1.5 Evaluating Binary Models' Predictive Accuracy; 4.1.6 ROC Charts; 4.1.7 Lift Chart; 4.2 Principal Component Analysis; 4.2.1 Overview; 4.2.2 Principal Components; 4.2.3 Generating Principal Components; 4.2.4 Interpretation of Principal Components; 4.3 Multiple Linear Regression; 4.3.1 Overview; 4.3.2 Generating Models; 4.3.3 Prediction; 4.3.4 Analysis of Residuals; 4.3.5 Standard Error; 4.3.6 Coefficient of Multiple Determination; 4.3.7 Testing the Model Significance; 4.3.8 Selecting and Transforming Variables 327 $a4.4 Discriminant Analysis 330 $aA hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction tha 606 $aData mining 606 $aInformation visualization 615 0$aData mining. 615 0$aInformation visualization. 676 $a005.74 700 $aMyatt$b Glenn J.$f1969-$0695403 701 $aJohnson$b Wayne P$0912685 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910145958103321 996 $aMaking sense of data II$92078894 997 $aUNINA