Introduction to Data Mining
Published on 7. July 2005
Book
Hardback
792 pages
978-0-321-32136-7 (ISBN)
Description
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.
More details
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
College/higher education
Dimensions
Height: 238 mm
Width: 199 mm
Thickness: 32 mm
Weight
1360 gr
ISBN-13
978-0-321-32136-7 (9780321321367)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Pang-Ning Tan | Michael Steinbach | Vipin Kumar
Introduction to Data Mining
Book
01/2018
2nd Edition
Pearson
€163.99
Shipment within 15-20 days
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
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