Introduction to Data Mining
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
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Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
1 Introduction1.1 What is Data Mining?1.2 Motivating Challenges1.3 The Origins of Data Mining1.4 Data Mining Tasks1.5 Scope and Organization of the Book1.6 Bibliographic Notes1.7 Exercises2 Data 2.1 Types of Data2.2 Data Quality2.3 Data Preprocessing2.4 Measures of Similarity and Dissimilarity2.5 Bibliographic Notes2.6 Exercises3 Exploring Data 3.1 The Iris Data Set3.2 Summary Statistics3.3 Visualization3.4 OLAP and Multidimensional Data Analysis3.5 Bibliographic Notes3.6 Exercises4 Classification: Basic Concepts, Decision Trees, and Model Evaluation 4.1 Preliminaries4.2 General Approach to Solving a Classification Problem4.3 Decision Tree Induction4.4 Model Overfitting4.5 Evaluating the Performance of a Classifier4.6 Methods for Comparing Classifiers4.7 Bibliographic Notes4.8 Exercises5 Classification: Alternative Techniques5.1 Rule-Based Classifier5.2 Nearest-Neighbor Classifiers5.3 Bayesian Classifiers5.4 Artificial Neural Network (ANN)5.5 Support Vector Machine (SVM)5.6 Ensemble Methods5.7 Class Imbalance Problem5.8 Multiclass Problem5.9 Bibliographic Notes5.10 Exercises6 Association Analysis: Basic Concepts and Algorithms 6.1 Problem Definition6.2 Frequent Itemset Generation6.3 Rule Generation6.4 Compact Representation of Frequent Itemsets6.5 Alternative Methods for Generating Frequent Itemsets6.6 FP-Growth Algorithm6.7 Evaluation of Association Patterns6.8 Effect of Skewed Support Distribution6.9 Bibliographic Notes6.10 Exercises7 Association Analysis: Advanced Concepts 7.1 Handling Categorical Attributes7.2 Handling Continuous Attributes7.3 Handling a Concept Hierarchy7.4 Sequential Patterns7.5 Subgraph Patterns7.6 Infrequent Patterns7.7 Bibliographic Notes7.8 Exercises8 Cluster Analysis: Basic Concepts and Algorithms8.1 Overview8.2 K-means8.3 Agglomerative Hierarchical Clustering8.4 DBSCAN8.5 Cluster Evaluation8.6 Bibliographic Notes8.7 Exercises9 Cluster Analysis: Additional Issues and Algorithms 9.1 Characteristics of Data, Clusters, and Clustering Algorithms9.2 Prototype-Based Clustering9.3 Density-Based Clustering9.4 Graph-Based Clustering9.5 Scalable Clustering Algorithms9.6 Which Clustering Algorithm?9.7 Bibliographic Notes9.8 Exercises10 Anomaly Detection 10.1 Preliminaries10.2 Statistical Approaches10.3 Proximity-Based Outlier Detection10.4 Density-Based Outlier Detection10.5 Clustering-Based Techniques10.6 Bibliographic Notes10.7 ExercisesAppendix A Linear AlgebraAppendix B Dimensionality ReductionAppendix C Probability and StatisticsAppendix D RegressionAppendix E OptimizationAuthor IndexSubject Index
