Front 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 -- Preamble -- A Short History of Statistics and Data Mining -- Modern Statistics: A Duality? -- Assumptions of the Parametric Model -- Two Views of Reality -- Aristotle -- Plato -- The Rise of Modern Statistical Analysis: The Second Generation -- Data, Data Everywhere -- Machine Learning Methods: The Third Generation -- Statistical Learning Theory: The Fourth Generation -- Postscript -- References -- Chapter 2: Theoretical Considerations for Data Mining -- Preamble -- The Scientific Method -- What Is Data Mining? -- A Theoretical Framework for the Data Mining Process -- Microeconomic Approach -- Inductive Database Approach -- Strengths of the Data Mining Process -- Customer-Centric Versus Account-Centric: A New Way to Look at Your Data -- The Physical Data Mart -- The Virtual Data Mart -- Householded Databases -- The Data Paradigm Shift -- Creation of the CAR -- Major Activities of Data Mining -- Major Challenges of Data Mining -- Examples of Data Mining Applications -- Major Issues in Data Mining -- General Requirements for Success in a Data Mining Project -- Example of a Data Mining Project: Classify a Bat's Species by Its Sound -- The Importance of Domain Knowledge -- Postscript -- Why Did Data Mining Arise? -- Some Caveats with Data Mining Solutions -- References -- Chapter 3: The Data Mining Process -- Preamble -- The Science of Data Mining -- The Approach to Understanding and Problem Solving -- CRISP-DM -- Business Understanding (Mostly Art).

Define the Business Objectives of the Data Mining Model -- Assess the Business Environment for Data Mining -- Formulate the Data Mining Goals and Objectives -- Data Understanding (Mostly Science) -- Data Acquisition -- Data Integration -- Data Description -- Data Quality Assessment -- Data Preparation (A Mixture of Art and Science) -- Modeling (A Mixture of Art and Science) -- Steps in the Modeling Phase of CRISP-DM -- Deployment (Mostly Art) -- Closing the Information Loop (Art) -- The Art of Data Mining -- Artistic Steps in Data Mining -- Postscript -- References -- Chapter 4: Data Understanding and Preparation -- Preamble -- Activities of Data Understanding and Preparation -- Definitions -- Issues That Should be Resolved -- Basic Issues That Must Be Resolved in Data Understanding -- Basic Issues That Must Be Resolved in Data Preparation -- Data Understanding -- Data Acquisition -- Data Extraction -- Data Description -- Data Assessment -- Data Profiling -- Data Cleansing -- Data Transformation -- Data Imputation -- Data Weighting and Balancing -- Data Filtering and Smoothing -- Data Abstraction -- Data Reduction -- Data Sampling -- Data Discretization -- Data Derivation -- Postscript -- References -- Chapter 5: Feature Selection -- Preamble -- Variables as Features -- Types of Feature Selections -- Feature Ranking Methods -- Gini Index -- Bi-variate Methods -- Multivariate Methods -- Complex Methods -- Subset Selection Methods -- The Other Two Ways of Using Feature Selection in STATISTICA: Interactive Workspace -- STATISTICA DMRecipe Method -- Postscript -- References -- Chapter 6: Accessory Tools for Doing Data Mining -- Preamble -- Data Access Tools -- Structured Query Language (SQL) Tools -- Extract, Transform, and Load (ETL) Capabilities -- Data Exploration Tools -- Basic Descriptive Statistics.

Combining Groups (Classes) for Predictive Data Mining -- Slicing/Dicing and Drilling Down into Data Sets/Results Spreadsheets -- Modeling Management Tools -- Data Miner Workspace Templates -- Modeling Analysis Tools -- Feature Selection -- Importance Plots of Variables -- In-Place Data Processing (IDP) -- Example: The IDP Facility of STATISTICA Data Miner -- How to Use the SQL -- Rapid Deployment of Predictive Models -- Model Monitors -- Postscript -- Bibliography -- 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) -- KXEN -- Basic Data Mining Algorithms -- Association Rules -- Neural Networks -- Radial Basis Function (RBF) Networks -- Automated Neural Nets -- Generalized Additive Models (GAMs) -- Outputs of GAMs -- Interpreting Results of GAMs -- Classification and Regression Trees (CART) -- Recursive Partitioning -- Pruning Trees -- General Comments about CART for Statisticians -- Advantages of CART over Other Decision Trees -- Uses of CART -- General Chaid Models -- Advantages of CHAID -- Disadvantages of CHAID -- Generalized EM and k-Means Cluster Analysis-An Overview -- k-Means Clustering -- EM Cluster Analysis -- Processing Steps of the EM Algorithm -- V-fold Cross-Validation as Applied to Clustering -- Postscript -- References -- Bibliography -- Chapter 8: Advanced Algorithms for Data Mining -- Preamble -- Advanced Data Mining Algorithms -- Interactive Trees -- Multivariate Adaptive Regression Splines (MARSplines) -- Statistical Learning Theory: Support Vector Machines -- Sequence, Association, and Link Analyses -- Independent Components Analysis (ICA) -- Kohonen Networks -- Characteristics of a Kohonen Network.

Quality Control Data Mining and Root Cause Analysis -- Image and Object Data Mining: Visualization and 3D-Medical and Other Scanning Imaging -- Postscript -- References -- Chapter 9: Text Mining and Natural Language Processing -- Preamble -- The Development of Text Mining -- A Practical Example: NTSB -- Goals of Text Mining of NTSB Accident Reports -- Drilling into Words of Interest -- Means with Error Plots -- Feature Selection Tool -- A Conclusion: Losing Control of the Aircraft in Bad Weather Is Often Fatal -- Summary -- Text Mining Concepts Used in Conducting Text Mining Studies -- Postscript -- References -- Chapter 10: The Three Most Common Data Mining Software Tools -- Preamble -- SPSS Clementine Overview -- Overall Organization of Clementine Components -- Organization of the Clementine Interface -- Clementine Interface Overview -- Setting the Default Directory -- SuperNodes -- Execution of Streams -- SAS-Enterprise Miner (SAS-EM) Overview -- Overall Organization of SAS-EM Version 5.3 Components -- Layout of the SAS-Enterprise Miner Window -- Various SAS-EM Menus, Dialogs, and Windows Useful During the Data Mining Process -- Software Requirements to Run SAS-EM 5.3 Software -- STATISTICA Data Miner, QC-Miner, and Text Miner Overview -- Overall Organization and Use of STATISTICA Data Miner -- Three Formats for Doing Data Mining in STATISTICA -- Postscript -- References -- Chapter 11: Classification -- Preamble -- What Is Classification? -- Initial Operations in Classification -- Major Issues with Classification -- What Is the Nature of the Data Set to Be Classified? -- How Accurate Does the Classification Have to Be? -- How Understandable Do the Classes Have to Be? -- Assumptions of Classification Procedures -- Numerical Variables Operate Best -- No Missing Values -- Variables Are Linear and Independent in Their Effects on the Target Variable.

Methods for Classification -- Nearest-Neighbor Classifiers -- Analyzing Imbalanced Data Sets with Machine Learning Programs -- CHAID -- Random Forests and Boosted Trees -- Logistic Regression -- Neural Networks -- Naive Bayesian Classifiers -- What Is the Best Algorithm for Classification? -- Postscript -- References -- Chapter 12: Numerical Prediction -- Preamble -- Linear Response Analysis and the Assumptions of the Parametric Model -- Parametric Statistical Analysis -- Assumptions of the Parametric Model -- The Assumption of Independency -- The Assumption of Normality -- Normality and the Central Limit Theorem -- The Assumption of Linearity -- Linear Regression -- Methods for Handling Variable Interactions in Linear Regression -- Collinearity among Variables in a Linear Regression -- The Concept of the Response Surface -- Generalized Linear Models (GLMs) -- Methods for Analyzing Nonlinear Relationships -- Nonlinear Regression and Estimation -- Logit and Probit Regression -- Poisson Regression -- Exponential Distributions -- Piecewise Linear Regression -- Data Mining and Machine Learning Algorithms Used in Numerical Prediction -- Numerical Prediction with C&RT -- Model Results Available in C&RT -- Advantages of Classification and Regression Trees (C&RT) Methods -- General Issues Related to C&RT -- Application to Mixed Models -- Neural Nets for Prediction -- Manual or Automated Operation? -- Structuring the Network for Manual Operation -- Modern Neural Nets Are "Gray Boxes" -- Example of Automated Neural Net Results -- Support Vector Machines (SVMs) and Other Kernel Learning Algorithms -- Postscript -- References -- Chapter 13: Model Evaluation and Enhancement -- Preamble -- Introduction -- Model Evaluation -- Splitting Data -- Avoiding Overfit Through Complexity Regularization -- Error Metric: Estimation -- Error Metric: Classification.

Error Metric: Ranking.