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Data Mining Introductory and Advanced Topics

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ISBN-10: 0130888923

ISBN-13: 9780130888921

Edition: 2003

Authors: Margaret H. Dunham

List price: $160.20
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Description:

Thorough in its coverage from basic to advanced topics, this book presents the key algorithms and techniques used in data mining. An emphasis is placed on the use of data mining concepts in real world applications with large database components. Includes unique chapters on Web mining, spatial mining, temporal mining, and prototypes and DM products. Separate case studies section highlights real world applications.
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Book details

List price: $160.20
Copyright year: 2003
Publisher: Prentice Hall PTR
Publication date: 8/22/2002
Binding: Paperback
Pages: 336
Size: 7.00" wide x 9.25" long x 0.55" tall
Weight: 1.386
Language: English

Preface
Introduction
Introduction
Basic Data Mining Tasks
Classification
Regression
Time Series Analysis
Prediction
Clustering
Summarization
Association Rules
Sequence Discovery
Data Mining Versus Knowledge Discovery in Databases
The Development of Data Mining
Data Mining Issues
Data Mining Metrics
Social Implications of Data Mining
Data Mining from a Database Perspective
The Future
Exercises
Bibliographic Notes
Related Concepts
Database/OLTP Systems
Fuzzy Sets and Fuzzy Logic
Information Retrieval
Decision Support Systems
Dimensional Modeling
Multidimensional Schemas
Indexing
Data Warehousing
OLAP
Web Search Engines
Statistics
Machine Learning
Pattern Matching
Summary
Exercises
Bibliographic Notes
Data Mining Techniques
Introduction
A Statistical Perspective on Data Mining
Point Estimation
Models Based on Summarization
Bayes Theorem
Hypothesis Testing
Regression and Correlation
Similarity Measures
Decision Trees
Neural Networks
Activation Functions
Genetic Algorithms
Exercises
Bibliographic Notes
Core Topics
Classification
Introduction
Issues in Classification
Statistical-Based Algorithms
Regression
Bayesian Classification
Distance-Based Algorithms
Simple Approach
K Nearest Neighbors
Decision Tree-Based Algorithms
ID3
C4.5 and C5.0
CART
Scalable DT Techniques
Neural Network-Based Algorithms
Propagation
NN Supervised Learning
Radial Basis Function Networks
Perceptrons
Rule-Based Algorithms
Generating Rules from a DT
Generating Rules from a Neural Net
Generating Rules Without a DT or NN
Combining Techniques
Summary
Exercises
Bibliographic Notes
Clustering
Introduction
Similarity and Distance Measures
Outliers
Hierarchical Algorithms
Agglomerative Algorithms
Divisive Clustering
Partitional Algorithms
Minimum Spanning Tree
Squared Error Clustering Algorithm
K-Means Clustering
Nearest Neighbor Algorithm
PAM Algorithm
Bond Energy Algorithm
Clustering with Genetic Algorithms
Clustering with Neural Networks
Clustering Large Databases
BIRCH
DBSCAN
CURE Algorithm
Clustering with Categorical Attributes
Comparison
Exercises
Bibliographic Notes
Association Rules
Introduction
Large Itemsets
Basic Algorithms
Apriori Algorithm
Sampling Algorithm
Partitioning
Parallel and Distributed Algorithms
Data Parallelism
Task Parallelism
Comparing Approaches
Incremental Rules
Advanced Association Rule Techniques
Generalized Association Rules
Multiple-Level Association Rules
Quantitative Association Rules
Using Multiple Minimum Supports
Correlation Rules
Measuring the Quality of Rules
Exercises
Bibliographic Notes
Advanced Topics
Web Mining
Introduction
Web Content Mining
Crawlers
Harvest System
Virtual Web View
Personalization
Web Structure Mining
PageRank
Clever
Web Usage Mining
Preprocessing
Data Structures
Pattern Discovery
Pattern Analysis
Exercises
Bibliographic Notes
Spatial Mining
Introduction
Spatial Data Overview
Spatial Queries
Spatial Data Structures
Thematic Maps
Image Databases
Spatial Data Mining Primitives
Generalization and Specialization
Progressive Refinement
Generalization
Nearest Neighbor
STING
Spatial Rules
Spatial Association Rules
Spatial Classification Algorithm
ID3 Extension
Spatial Decision Tree
Spatial Clustering Algorithms
CLARANS Extensions
SD(CLARANS)
DBCLASD
BANG
WaveCluster
Approximation
Exercises
Bibliographic Notes
Temporal Mining
Introduction
Modeling Temporal Events
Time Series
Time Series Analysis
Trend Analysis
Transformation
Similarity
Prediction
Pattern Detection
String Matching
Sequences
AprioriAll
SPADE
Generalization
Feature Extraction
Temporal Association Rules
Intertransaction Rules
Episode Rules
Trend Dependencies
Sequence Association Rules
Calendric Association Rules
Exercises
Bibliographic Notes
Appendices
Data Mining Products
Bibliographic Notes
Bibliography
Index
About the Author