{"product_id":"visual-data-mining-isbn-9781119967545","title":"Visual Data Mining","description":"\u003cb\u003eA visual approach to data mining.\u003c\/b\u003e \u003cp\u003eData mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed.\u003c\/p\u003e \u003cp\u003eThis book introduces a visual methodology for data mining demonstrating the application of methodology along with a sequence of exercises using VisMiner. VisMiner has been developed by the author and provides a powerful visual data mining tool enabling the reader to see the data that they are working on and to visually evaluate the models created from the data.\u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents visual support for all phases of data mining including dataset preparation.\u003c\/li\u003e \u003cli\u003eProvides a comprehensive set of non-trivial datasets and problems with accompanying software.\u003c\/li\u003e \u003cli\u003eFeatures 3-D visualizations of multi-dimensional datasets.\u003c\/li\u003e \u003cli\u003eGives support for spatial data analysis with GIS like features.\u003c\/li\u003e \u003cli\u003eDescribes data mining algorithms with guidance on when and how to use.\u003c\/li\u003e \u003cli\u003eAccompanied by VisMiner, a visual software tool for data mining, developed specifically to bridge the gap between theory and practice.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eVisual Data Mining: The VisMiner Approach\u003c\/i\u003e is designed as a hands-on work book to introduce the methodologies to students in data mining, advanced statistics, and business intelligence courses. This book provides a set of tutorials, exercises, and case studies that support students in learning data mining processes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIn praise of the VisMiner approach:\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\"What we discovered among students was that the visualization concepts and tools brought the analysis alive in a way that was broadly understood and could be used to make sound decisions with greater certainty about the outcomes\"\u003cbr\u003e—\u003cb\u003eDr. James V. Hansen\u003c\/b\u003e, J. Owen Cherrington Professor, Marriott School, Brigham Young University, USA\u003c\/p\u003e \u003cp\u003e\"Students learn best when they are able to visualize relationships between data and results during the data mining process. VisMiner is easy to learn and yet offers great visualization capabilities throughout the data mining process. My students liked it very much and so did I.\"\u003cbr\u003e—\u003cb\u003eDr. Douglas Dean\u003c\/b\u003e, Assoc. Professor of Information Systems, Marriott School, Brigham Young University, USA\u003c\/p\u003e  Preface ix  \u003cp\u003eAcknowledgments xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Mining Objectives 1\u003c\/p\u003e \u003cp\u003eIntroduction to VisMiner 2\u003c\/p\u003e \u003cp\u003eThe Data Mining Process 3\u003c\/p\u003e \u003cp\u003eInitial Data Exploration 4\u003c\/p\u003e \u003cp\u003eDataset Preparation 5\u003c\/p\u003e \u003cp\u003eAlgorithm Selection and Application 8\u003c\/p\u003e \u003cp\u003eModel Evaluation 8\u003c\/p\u003e \u003cp\u003eSummary 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Initial Data Exploration and Dataset Preparation Using VisMiner 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Rationale for Visualizations 11\u003c\/p\u003e \u003cp\u003eTutorial – Using VisMiner 13\u003c\/p\u003e \u003cp\u003eInitializing VisMiner 13\u003c\/p\u003e \u003cp\u003eInitializing the Slave Computers 14\u003c\/p\u003e \u003cp\u003eOpening a Dataset 16\u003c\/p\u003e \u003cp\u003eViewing Summary Statistics 16\u003c\/p\u003e \u003cp\u003eExercise 2.1 17\u003c\/p\u003e \u003cp\u003eThe Correlation Matrix 18\u003c\/p\u003e \u003cp\u003eExercise 2.2 20\u003c\/p\u003e \u003cp\u003eThe Histogram 21\u003c\/p\u003e \u003cp\u003eThe Scatter Plot 23\u003c\/p\u003e \u003cp\u003eExercise 2.3 28\u003c\/p\u003e \u003cp\u003eThe Parallel Coordinate Plot 28\u003c\/p\u003e \u003cp\u003eExercise 2.4 33\u003c\/p\u003e \u003cp\u003eExtracting Sub-populations Using the Parallel Coordinate Plot 37\u003c\/p\u003e \u003cp\u003eExercise 2.5 41\u003c\/p\u003e \u003cp\u003eThe Table Viewer 42\u003c\/p\u003e \u003cp\u003eThe Boundary Data Viewer 43\u003c\/p\u003e \u003cp\u003eExercise 2.6 47\u003c\/p\u003e \u003cp\u003eThe Boundary Data Viewer with Temporal Data 47\u003c\/p\u003e \u003cp\u003eExercise 2.7 49\u003c\/p\u003e \u003cp\u003eSummary 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Advanced Topics in Initial Exploration and Dataset Preparation Using VisMiner 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMissing Values 51\u003c\/p\u003e \u003cp\u003eMissing Values – An Example 53\u003c\/p\u003e \u003cp\u003eExploration Using the Location Plot 56\u003c\/p\u003e \u003cp\u003eExercise 3.1 61\u003c\/p\u003e \u003cp\u003eDataset Preparation – Creating Computed Columns 61\u003c\/p\u003e \u003cp\u003eExercise 3.2 63\u003c\/p\u003e \u003cp\u003eAggregating Data for Observation Reduction 63\u003c\/p\u003e \u003cp\u003eExercise 3.3 65\u003c\/p\u003e \u003cp\u003eCombining Datasets 66\u003c\/p\u003e \u003cp\u003eExercise 3.4 67\u003c\/p\u003e \u003cp\u003eOutliers and Data Validation 68\u003c\/p\u003e \u003cp\u003eRange Checks 69\u003c\/p\u003e \u003cp\u003eFixed Range Outliers 69\u003c\/p\u003e \u003cp\u003eDistribution Based Outliers 70\u003c\/p\u003e \u003cp\u003eComputed Checks 72\u003c\/p\u003e \u003cp\u003eExercise 3.5 74\u003c\/p\u003e \u003cp\u003eFeasibility and Consistency Checks 74\u003c\/p\u003e \u003cp\u003eData Correction Outside of VisMiner 75\u003c\/p\u003e \u003cp\u003eDistribution Consistency 76\u003c\/p\u003e \u003cp\u003ePattern Checks 77\u003c\/p\u003e \u003cp\u003eA Pattern Check of Experimental Data 80\u003c\/p\u003e \u003cp\u003eExercise 3.6 81\u003c\/p\u003e \u003cp\u003eSummary 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Prediction Algorithms for Data Mining 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDecision Trees 84\u003c\/p\u003e \u003cp\u003eStopping the Splitting Process 86\u003c\/p\u003e \u003cp\u003eA Decision Tree Example 87\u003c\/p\u003e \u003cp\u003eUsing Decision Trees 89\u003c\/p\u003e \u003cp\u003eDecision Tree Advantages 89\u003c\/p\u003e \u003cp\u003eLimitations 90\u003c\/p\u003e \u003cp\u003eArtificial Neural Networks 90\u003c\/p\u003e \u003cp\u003eOverfitting the Model 93\u003c\/p\u003e \u003cp\u003eMoving Beyond Local Optima 94\u003c\/p\u003e \u003cp\u003eANN Advantages and Limitations 96\u003c\/p\u003e \u003cp\u003eSupport Vector Machines 97\u003c\/p\u003e \u003cp\u003eData Transformations 99\u003c\/p\u003e \u003cp\u003eMoving Beyond Two-dimensional Predictors 100\u003c\/p\u003e \u003cp\u003eSVM Advantages and Limitations 100\u003c\/p\u003e \u003cp\u003eSummary 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Classification Models in VisMiner 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDataset Preparation 103\u003c\/p\u003e \u003cp\u003eTutorial – Building and Evaluating Classification Models 104\u003c\/p\u003e \u003cp\u003eModel Evaluation 104\u003c\/p\u003e \u003cp\u003eExercise 5.1 109\u003c\/p\u003e \u003cp\u003ePrediction Likelihoods 109\u003c\/p\u003e \u003cp\u003eClassification Model Performance 113\u003c\/p\u003e \u003cp\u003eInterpreting the ROC Curve 119\u003c\/p\u003e \u003cp\u003eClassification Ensembles 124\u003c\/p\u003e \u003cp\u003eModel Application 125\u003c\/p\u003e \u003cp\u003eSummary 127\u003c\/p\u003e \u003cp\u003eExercise 5.2 128\u003c\/p\u003e \u003cp\u003eExercise 5.3 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Regression Analysis 131\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Regression Model 131\u003c\/p\u003e \u003cp\u003eCorrelation and Causation 132\u003c\/p\u003e \u003cp\u003eAlgorithms for Regression Analysis 133\u003c\/p\u003e \u003cp\u003eAssessing Regression Model Performance 133\u003c\/p\u003e \u003cp\u003eModel Validity 135\u003c\/p\u003e \u003cp\u003eLooking Beyond R2 135\u003c\/p\u003e \u003cp\u003ePolynomial Regression 137\u003c\/p\u003e \u003cp\u003eArtificial Neural Networks for Regression Analysis 137\u003c\/p\u003e \u003cp\u003eDataset Preparation 137\u003c\/p\u003e \u003cp\u003eTutorial 138\u003c\/p\u003e \u003cp\u003eA Regression Model for Home Appraisal 139\u003c\/p\u003e \u003cp\u003eModeling with the Right Set of Observations 139\u003c\/p\u003e \u003cp\u003eExercise 6.1 145\u003c\/p\u003e \u003cp\u003eANN Modeling 145\u003c\/p\u003e \u003cp\u003eThe Advantage of ANN Regression 148\u003c\/p\u003e \u003cp\u003eTop-Down Attribute Selection 149\u003c\/p\u003e \u003cp\u003eIssues in Model Interpretation 150\u003c\/p\u003e \u003cp\u003eModel Validation 152\u003c\/p\u003e \u003cp\u003eModel Application 153\u003c\/p\u003e \u003cp\u003eSummary 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Cluster Analysis 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 155\u003c\/p\u003e \u003cp\u003eAlgorithms for Cluster Analysis 158\u003c\/p\u003e \u003cp\u003eIssues with K-Means Clustering Process 158\u003c\/p\u003e \u003cp\u003eHierarchical Clustering 159\u003c\/p\u003e \u003cp\u003eMeasures of Cluster and Clustering Quality 159\u003c\/p\u003e \u003cp\u003eSilhouette Coefficient 161\u003c\/p\u003e \u003cp\u003eCorrelation Coefficient 161\u003c\/p\u003e \u003cp\u003eSelf-Organizing Maps (SOM) 161\u003c\/p\u003e \u003cp\u003eSelf-Organizing Maps in VisMiner 163\u003c\/p\u003e \u003cp\u003eChoosing the Grid Dimensions 168\u003c\/p\u003e \u003cp\u003eAdvantages of a 3-D Grid 169\u003c\/p\u003e \u003cp\u003eExtracting Subsets from a Clustering 170\u003c\/p\u003e \u003cp\u003eSummary 173\u003c\/p\u003e \u003cp\u003eAppendix A VisMiner Reference by Task 175\u003c\/p\u003e \u003cp\u003eAppendix B VisMiner Task\/Tool Matrix 187\u003c\/p\u003e \u003cp\u003eAppendix C IP Address Look-up 189\u003c\/p\u003e \u003cp\u003eIndex 191\u003c\/p\u003e \u003cp\u003e\"VisMiner has been developed by the author and provides a powerful visual data mining tool enabling the reader to see the data that they are working on and to visually evaluate the models created from the data.\" (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2016)\u003c\/p\u003e \u003cp\u003e\u003cb\u003eRussell K. Anderson\u003c\/b\u003e, Information \u0026amp; Decision Management Department, West Texas A\u0026amp;M University, USA.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eA visual approach to data mining.\u003c\/b\u003e \u003c\/p\u003e \u003cp\u003eData mining has been defined as the search for useful and previously unknown patterns in large datasets, yet when faced with the task of mining a large dataset, it is not always obvious where to start and how to proceed. \u003c\/p\u003e \u003cp\u003eThis book introduces a visual methodology for data mining demonstrating the application of methodology along with a sequence of exercises using VisMiner. VisMiner has been developed by the author and provides a powerful visual data mining tool enabling the reader to see the data that they are working on and to visually evaluate the models created from the data. \u003c\/p\u003e \u003cp\u003eKey features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents visual support for all phases of data mining including dataset preparation.\u003c\/li\u003e \u003cli\u003eProvides a comprehensive set of non-trivial datasets and problems with accompanying software.\u003c\/li\u003e \u003cli\u003eFeatures 3-D visualizations of multi-dimensional datasets.\u003c\/li\u003e \u003cli\u003eGives support for spatial data analysis with GIS like features.\u003c\/li\u003e \u003cli\u003eDescribes data mining algorithms with guidance on when and how to use.\u003c\/li\u003e \u003cli\u003eAccompanied by VisMiner, a visual software tool for data mining, developed specifically to bridge the gap between theory and practice.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eVisual Data Mining: The VisMiner Approach\u003c\/i\u003e is designed as a hands-on work book to introduce the methodologies to students in data mining, advanced statistics, and business intelligence courses. This book provides a set of tutorials, exercises, and case studies that support students in learning data mining processes.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIn praise of the VisMiner approach:\u003c\/b\u003e\u003ci\u003e \u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e“What we discovered among students was that the visualization concepts and tools brought the analysis alive in a way that was broadly understood and could be used to make sound decisions with greater certainty about the outcomes”—Dr. James V. Hansen, J. Owen Cherrington Professor, Marriott School, Brigham Young University, USA\u003c\/p\u003e “Students learn best when they are able to visualize relationships between data and results during the data mining process. VisMiner is easy to learn and yet offers great visualization capabilities throughout the data mining process. My students liked it very much and so did I.” —Dr. Douglas Dean, Assoc. 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