Introduction to Data Mining: Pearson New International Edition
Published on 17. July 2013
Book
Paperback/Softback
736 pages
978-1-292-02615-2 (ISBN)
Description
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. 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. Quotes This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts. -Sanjay Ranka, University of Florida In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules). -Mohammed Zaki, Rensselaer Polytechnic Institute
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Pang-Ning Tan | Michael Steinbach | Vipin Kumar
Introduction to Data Mining: Pearson New International Edition
Pearson New International Edition PDF eBook
E-Book
08/2013
1st Edition
Pearson Education Limited
€53.49
Available for download
Previous edition
Book
05/2006
Pearson
€148.55
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Content
1 Introduction
1.1 What is Data Mining?
1.2 Motivating Challenges
1.3 The Origins of Data Mining
1.4 Data Mining Tasks
1.5 Scope and Organization of the Book
1.6 Bibliographic Notes
1.7 Exercises
2 Data
2.1 Types of Data
2.2 Data Quality
2.3 Data Preprocessing
2.4 Measures of Similarity and Dissimilarity
2.5 Bibliographic Notes
2.6 Exercises
3 Exploring Data
3.1 The Iris Data Set
3.2 Summary Statistics
3.3 Visualization
3.4 OLAP and Multidimensional Data Analysis
3.5 Bibliographic Notes
3.6 Exercises
4 Classification: Basic Concepts, Decision Trees, and Model Evaluation
4.1 Preliminaries
4.2 General Approach to Solving a Classification Problem
4.3 Decision Tree Induction
4.4 Model Overfitting
4.5 Evaluating the Performance of a Classifier
4.6 Methods for Comparing Classifiers
4.7 Bibliographic Notes
4.8 Exercises
5 Classification: Alternative Techniques
5.1 Rule-Based Classifier
5.2 Nearest-Neighbor Classifiers
5.3 Bayesian Classifiers
5.4 Artificial Neural Network (ANN)
5.5 Support Vector Machine (SVM)
5.6 Ensemble Methods
5.7 Class Imbalance Problem
5.8 Multiclass Problem
5.9 Bibliographic Notes
5.10 Exercises
6 Association Analysis: Basic Concepts and Algorithms
6.1 Problem Definition
6.2 Frequent Itemset Generation
6.3 Rule Generation
6.4 Compact Representation of Frequent Itemsets
6.5 Alternative Methods for Generating Frequent Itemsets
6.6 FP-Growth Algorithm
6.7 Evaluation of Association Patterns
6.8 Effect of Skewed Support Distribution
6.9 Bibliographic Notes
6.10 Exercises
9 Cluster Analysis: Basic Concepts and Algorithms
8.1 Overview
8.2 K-means
8.3 Agglomerative Hierarchical Clustering
8.4 DBSCAN
8.5 Cluster Evaluation
8.6 Bibliographic Notes
8.7 Exercises
10 Cluster Analysis: Additional Issues and Algorithms
9.1 Characteristics of Data, Clusters, and Clustering Algorithms
9.2 Prototype-Based Clustering
9.3 Density-Based Clustering
9.4 Graph-Based Clustering
9.5 Scalable Clustering Algorithms
9.6 Which Clustering Algorithm?
9.7 Bibliographic Notes
9.8 Exercises
11 Anomaly Detection
10.1 Preliminaries
10.2 Statistical Approaches
10.3 Proximity-Based Outlier Detection
10.4 Density-Based Outlier Detection
10.5 Clustering-Based Techniques
10.6 Bibliographic Notes
10.7 Exercises
Appendix B Dimensionality Reduction
Appendix D Regression
Appendix E Optimization
Author Index
Subject Index