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Introduction to Data Mining

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ISBN-10: 0321321367

ISBN-13: 9780321321367

Edition: 2006

Authors: Vipin Kumar, Michael Steinbach, Pang-Ning Tan

List price: $159.99
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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.
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Book details

List price: $159.99
Copyright year: 2006
Publisher: Addison Wesley
Publication date: 5/2/2005
Binding: Hardcover
Pages: 792
Size: 7.50" wide x 9.25" long x 1.25" tall
Weight: 3.190
Language: English

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