Data Mining: Introductory and Advanced Topics

Name: Data Mining: Introductory and Advanced Topics
Price: 4.68 USD
Availability: InStock
ISBN: 9780130888921

ISBN-10: 0130888923

ISBN-13: 9780130888921

Edition: 2003

Authors: Margaret Dunham

List price: $159.99

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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.

Book details

List price: $159.99
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