Addison-Wesley / Prentice Hall
Computer Science
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Introduction to Data Mining
ISBN-10: 0321321367
ISBN-13: 9780321321367
Publisher: Addison-Wesley
Copyright: 2006
Format: Cloth; 769 pp
Published: 05/02/2005
Status: Instock
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
- Provides both theoretical and practical coverage of all data mining topics.
- Includes extensive number of integrated examples and figures.
- Offers instructor resources including solutions for exercises and complete set of lecture slides.
- Assumes only a modest statistics or mathematics background, and no database knowledge is needed.
- Topics covered include; predictive modeling, association analysis, clustering, anomaly detection, visualization.
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
7 Association Analysis: Advanced Concepts
7.1 Handling Categorical Attributes
7.2 Handling Continuous Attributes
7.3 Handling a Concept Hierarchy
7.4 Sequential Patterns
7.5 Subgraph Patterns
7.6 Infrequent Patterns
7.7 Bibliographic Notes
7.8 Exercises
8 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
9 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
10 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 A Linear Algebra
Appendix B Dimensionality Reduction
Appendix C Probability and Statistics
Appendix D Regression
Appendix E Optimization
Author Index
Subject Index
Gradiance for Introduction to Data Mining, 5/E
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | Website | Instock
ISBN-10: 0321476182 |
ISBN-13: 9780321476180
Online purchase price: $24.00
Author's Companion Website
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | On-line Supplement | Instock
ISBN-10: 0321335678 |
ISBN-13: 9780321335678
Author's Companion Website
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | On-line Supplement | Instock
ISBN-10: 0321335678 |
ISBN-13: 9780321335678
Gradiance for Introduction to Data Mining, 5/E
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | Website | Instock
ISBN-10: 0321476182 |
ISBN-13: 9780321476180
Online purchase price: $24.00
Instructor's Online Errata List
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | On-line Supplement |
Estimated Availability : 07/01/2005
ISBN-10: 0321398513 |
ISBN-13: 9780321398512
Online Instructor Solutions Manual
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | On-line Supplement | Instock
ISBN-10: 0321335651 |
ISBN-13: 9780321335654
View Downloadable Files
PowerPoint Slides
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | On-line Supplement | Instock
ISBN-10: 032133566X |
ISBN-13: 9780321335661
View Downloadable Files
Author's Companion Website
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | On-line Supplement | Instock
ISBN-10: 0321335678 |
ISBN-13: 9780321335678
CS Support-Student Support Material
Addison-Wesley
©2008 | Addison-Wesley | On-line Supplement | Instock
ISBN-10: 0321446852 |
ISBN-13: 9780321446855
Gradiance for Introduction to Data Mining, 5/E
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | Website | Instock
ISBN-10: 0321476182 |
ISBN-13: 9780321476180
Online purchase price: $24.00
Gradiance for Introduction to Data Mining, 5/E
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | Website | Instock
ISBN-10: 0321476182 |
ISBN-13: 9780321476180
Online purchase price: $24.00
Author's Companion Website
Tan, Steinbach & Kumar
©2006 | Addison-Wesley | On-line Supplement | Instock
ISBN-10: 0321335678 |
ISBN-13: 9780321335678
Pearson Higher Education offers special pricing when you choose to package your text with other student resources. If you're interested in creating a cost-saving package for your students, browse our available packages below, or contact your Pearson Higher Education representative to create your own package.
Package ISBN-10: 0321478207 | ISBN-13: 9780321478207
©2006 | Instock (Additional assembly time required)
Suggested retail price: $121.00 Buy from myPearsonStore
This package contains:
- Gradiance Access Card for Introduction to Data Mining
