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| Introduction | |
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| What is Data Mining? | |
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| Motivating Challenges | |
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| The Origins of Data Mining | |
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| Data Mining Tasks | |
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| Scope and Organization of the Book | |
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| Bibliographic Notes | |
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| Exercises | |
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| Data | |
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| Types of Data | |
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| Data Quality | |
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| Data Preprocessing | |
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| Measures of Similarity and Dissimilarity | |
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| Bibliographic Notes | |
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| Exercises | |
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| Exploring Data | |
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| The Iris Data Set | |
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| Summary Statistics | |
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| Visualization | |
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| OLAP and Multidimensional Data Analysis | |
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| Bibliographic Notes | |
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| Exercises | |
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| Classification: Basic Concepts, Decision Trees, and Model Evaluation | |
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| Preliminaries | |
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| General Approach to Solving a Classification Problem | |
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| Decision Tree Induction | |
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| Model Overfitting | |
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| Evaluating the Performance of a Classifier | |
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| Methods for Comparing Classifiers | |
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| Bibliographic Notes | |
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| Exercises | |
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| Classification: Alternative Techniques | |
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| Rule-Based Classifier | |
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| Nearest-Neighbor Classifiers | |
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| Bayesian Classifiers | |
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| Artificial Neural Network (ANN) | |
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| Support Vector Machine (SVM) | |
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| Ensemble Methods | |
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| Class Imbalance Problem | |
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| Multiclass Problem | |
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| Bibliographic Notes | |
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| Exercises | |
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| Association Analysis: Basic Concepts and Algorithms | |
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| Problem Definition | |
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| Frequent Itemset Generation | |
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| Rule Generation | |
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| Compact Representation of Frequent Itemsets | |
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| Alternative Methods for Generating Frequent Itemsets | |
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| FP-Growth Algorithm | |
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| Evaluation of Association Patterns | |
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| Effect of Skewed Support Distribution | |
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| Bibliographic Notes | |
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| Exercises | |
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| Association Analysis: Advanced Concepts | |
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| Handling Categorical Attributes | |
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| Handling Continuous Attributes | |
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| Handling a Concept Hierarchy | |
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| Sequential Patterns | |
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| Subgraph Patterns | |
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| Infrequent Patterns | |
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| Bibliographic Notes | |
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| Exercises | |
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| Cluster Analysis: Basic Concepts and Algorithms | |
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| Overview | |
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| K-means | |
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| Agglomerative Hierarchical Clustering | |
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| DBSCAN | |
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| Cluster Evaluation | |
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| Bibliographic Notes | |
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| Exercises | |
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| Cluster Analysis: Additional Issues and Algorithms | |
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| Characteristics of Data, Clusters, and Clustering Algorithms | |
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| Prototype-Based Clustering | |
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| Density-Based Clustering | |
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| Graph-Based Clustering | |
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| Scalable Clustering Algorithms | |
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| Which Clustering Algorithm? | |
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| Bibliographic Notes | |
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| Exercises | |
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| Anomaly Detection | |
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| Preliminaries | |
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| Statistical Approaches | |
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| Proximity-Based Outlier Detection | |
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| Density-Based Outlier Detection | |
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| Clustering-Based Techniques | |
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| Bibliographic Notes | |
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| Exercises | |
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| Linear Algebra | |
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| Dimensionality Reduction | |
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| Probability and Statistics | |
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| Regression | |
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| Optimization | |
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| Author Index | |
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| Subject Index | |