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The Resource Data mining and analysis : fundamental concepts and algorithms, Mohammed J. Zaki, Wagner Meira, Jr

Data mining and analysis : fundamental concepts and algorithms, Mohammed J. Zaki, Wagner Meira, Jr

Label
Data mining and analysis : fundamental concepts and algorithms
Title
Data mining and analysis
Title remainder
fundamental concepts and algorithms
Statement of responsibility
Mohammed J. Zaki, Wagner Meira, Jr
Creator
Contributor
Author
Subject
Language
eng
Cataloging source
DLC
http://library.link/vocab/creatorDate
1971-
http://library.link/vocab/creatorName
Zaki, Mohammed J.
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorDate
1967-
http://library.link/vocab/relatedWorkOrContributorName
Meira, Wagner
http://library.link/vocab/subjectName
Data mining
Label
Data mining and analysis : fundamental concepts and algorithms, Mohammed J. Zaki, Wagner Meira, Jr
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
  • Data: Algebraic and Geometric View
  • Exercises
  • 7.
  • Dimensionality Reduction
  • 7.1.
  • Background
  • 7.2.
  • Principal Component Analysis
  • 7.3.
  • Kernel Principal Component Analysis
  • 7.4.
  • 1.4.
  • Singular Value Decomposition
  • 7.5.
  • Further Reading
  • 7.6.
  • Exercises
  • pt. TWO
  • FREQUENT PATTERN MINING
  • 8.
  • Itemset Mining
  • 8.1.
  • Data: Probabilistic View
  • Frequent Itemsets and Association Rules
  • 8.2.
  • Itemset Mining Algorithms
  • 8.3.
  • Generating Association Rules
  • 8.4.
  • Further Reading
  • 8.5.
  • Exercises
  • 9.
  • 1.5.
  • Summarizing Itemsets
  • 9.1.
  • Maximal and Closed Frequent Itemsets
  • 9.2.
  • Mining Maximal Frequent Itemsets: GenMax Algorithm
  • 9.3.
  • Mining Closed Frequent Itemsets: Charm Algorithm
  • 9.4.
  • Nonderivable Itemsets
  • 9.5.
  • Data Mining
  • Further Reading
  • 9.6.
  • Exercises
  • 10.
  • Sequence Mining
  • 10.1.
  • Frequent Sequences
  • 10.2.
  • Mining Frequent Sequences
  • 10.3.
  • 1.6.
  • Substring Mining via Suffix Trees
  • 10.4.
  • Further Reading
  • 10.5.
  • Exercises
  • 11.
  • Graph Pattern Mining
  • 11.1.
  • Isomorphism and Support
  • 11.2.
  • Further Reading
  • Candidate Generation
  • 11.3.
  • The gSpan Algorithm
  • 11.4.
  • Further Reading
  • 11.5.
  • Exercises
  • 12.
  • Pattern and Rule Assessment
  • 12.1.
  • 1.7.
  • Rule and Pattern Assessment Measures
  • 12.2.
  • Significance Testing and Confidence Intervals
  • 12.3.
  • Further Reading
  • 12.4.
  • Exercises
  • pt. THREE
  • CLUSTERING
  • 13.
  • Exercises
  • Representative-based Clustering
  • 13.1.
  • K-means Algorithm
  • 13.2.
  • Kernel K-means
  • 13.3.
  • Expectation-Maximization Clustering
  • 13.4.
  • Further Reading
  • 13.5.
  • pt. ONE
  • Exercises
  • 14.
  • Hierarchical Clustering
  • 14.1.
  • Preliminaries
  • 14.2.
  • Agglomerative Hierarchical Clustering
  • 14.3.
  • Further Reading
  • 14.4.
  • Machine generated contents note:
  • DATA ANALYSIS FOUNDATIONS
  • Exercises and Projects
  • 15.
  • Density-based Clustering
  • 15.1.
  • The DBSCAN Algorithm
  • 15.2.
  • Kernel Density Estimation
  • 15.3.
  • Density-based Clustering: DENCLUE
  • 15.4.
  • 2.
  • Further Reading
  • 15.5.
  • Exercises
  • 16.
  • Spectral and Graph Clustering
  • 16.1.
  • Graphs and Matrices
  • 16.2.
  • Clustering as Graph Cuts
  • 16.3.
  • Numeric Attributes
  • Markov Clustering
  • 16.4.
  • Further Reading
  • 16.5.
  • Exercises
  • 17.
  • Clustering Validation
  • 17.1.
  • External Measures
  • 17.2.
  • 2.1.
  • Internal Measures
  • 17.3.
  • Relative Measures
  • 17.4.
  • Further Reading
  • 17.5.
  • Exercises
  • pt. FOUR
  • CLASSIFICATION
  • 18.
  • Univariate Analysis
  • Probabilistic Classification
  • 18.1.
  • Bayes Classifier
  • 18.2.
  • Naive Bayes Classifier
  • 18.3.
  • K Nearest Neighbors Classifier
  • 18.4.
  • Further Reading
  • 18.5.
  • 2.2.
  • Exercises
  • 19.
  • Decision Tree Classifier
  • 19.1.
  • Decision Trees
  • 19.2.
  • Decision Tree Algorithm
  • 19.3.
  • Further Reading
  • 19.4.
  • Bivariate Analysis
  • Exercises
  • 20.
  • Linear Discriminant Analysis
  • 20.1.
  • Optimal Linear Discriminant
  • 20.2.
  • Kernel Discriminant Analysis
  • 20.3.
  • Further Reading
  • 20.4.
  • 2.3.
  • Exercises
  • 21.
  • Support Vector Machines
  • 21.1.
  • Support Vectors and Margins
  • 21.2.
  • SVM: Linear and Separable Case
  • 21.3.
  • Soft Margin SVM: Linear and Nonseparable Case
  • 21.4.
  • Multivariate Analysis
  • Kernel SVM: Nonlinear Case
  • 21.5.
  • SVM Training Algorithms
  • 21.6.
  • Further Reading
  • 21.7.
  • Exercises
  • 22.
  • Classification Assessment
  • 22.1.
  • 2.4.
  • Classification Performance Measures
  • 22.2.
  • Classifier Evaluation
  • 22.3.
  • Bias-Variance Decomposition
  • 22.4.
  • Further Reading
  • 22.5.
  • Exercises
  • 1.
  • Data Normalization
  • 2.5.
  • Normal Distribution
  • 2.6.
  • Further Reading
  • 2.7.
  • Exercises
  • 3.
  • Categorical Attributes
  • 3.1.
  • Data Mining and Analysis
  • Univariate Analysis
  • 3.2.
  • Bivariate Analysis
  • 3.3.
  • Multivariate Analysis
  • 3.4.
  • Distance and Angle
  • 3.5.
  • Discretization
  • 3.6.
  • 1.1.
  • Further Reading
  • 3.7.
  • Exercises
  • 4.
  • Graph Data
  • 4.1.
  • Graph Concepts
  • 4.2.
  • Topological Attributes
  • 4.3.
  • Data Matrix
  • Centrality Analysis
  • 4.4.
  • Graph Models
  • 4.5.
  • Further Reading
  • 4.6.
  • Exercises
  • 5.
  • Kernel Methods
  • 5.1.
  • 1.2.
  • Kernel Matrix
  • 5.2.
  • Vector Kernels
  • 5.3.
  • Basic Kernel Operations in Feature Space
  • 5.4.
  • Kernels for Complex Objects
  • 5.5.
  • Further Reading
  • 5.6.
  • Attributes
  • Exercises
  • 6.
  • High-dimensional Data
  • 6.1.
  • High-dimensional Objects
  • 6.2.
  • High-dimensional Volumes
  • 6.3.
  • Hypersphere Inscribed within Hypercube
  • 6.4.
  • 1.3.
  • Volume of Thin Hypersphere Shell
  • 6.5.
  • Diagonals in Hyperspace
  • 6.6.
  • Density of the Multivariate Normal
  • 6.7.
  • Appendix: Derivation of Hypersphere Volume
  • 6.8.
  • Further Reading
  • 6.9.
Control code
ocn858610961
Dimensions
27 cm
Extent
xi, 593 pages
Isbn
9780521766333
Isbn Type
(hardback : alk. paper)
Lccn
2013037544
Media category
unmediated
Media MARC source
rdamedia
System control number
(OCoLC)858610961
Label
Data mining and analysis : fundamental concepts and algorithms, Mohammed J. Zaki, Wagner Meira, Jr
Publication
Bibliography note
Includes bibliographical references and index
Carrier category
volume
Carrier MARC source
rdacarrier
Content category
text
Content type MARC source
rdacontent
Contents
  • Data: Algebraic and Geometric View
  • Exercises
  • 7.
  • Dimensionality Reduction
  • 7.1.
  • Background
  • 7.2.
  • Principal Component Analysis
  • 7.3.
  • Kernel Principal Component Analysis
  • 7.4.
  • 1.4.
  • Singular Value Decomposition
  • 7.5.
  • Further Reading
  • 7.6.
  • Exercises
  • pt. TWO
  • FREQUENT PATTERN MINING
  • 8.
  • Itemset Mining
  • 8.1.
  • Data: Probabilistic View
  • Frequent Itemsets and Association Rules
  • 8.2.
  • Itemset Mining Algorithms
  • 8.3.
  • Generating Association Rules
  • 8.4.
  • Further Reading
  • 8.5.
  • Exercises
  • 9.
  • 1.5.
  • Summarizing Itemsets
  • 9.1.
  • Maximal and Closed Frequent Itemsets
  • 9.2.
  • Mining Maximal Frequent Itemsets: GenMax Algorithm
  • 9.3.
  • Mining Closed Frequent Itemsets: Charm Algorithm
  • 9.4.
  • Nonderivable Itemsets
  • 9.5.
  • Data Mining
  • Further Reading
  • 9.6.
  • Exercises
  • 10.
  • Sequence Mining
  • 10.1.
  • Frequent Sequences
  • 10.2.
  • Mining Frequent Sequences
  • 10.3.
  • 1.6.
  • Substring Mining via Suffix Trees
  • 10.4.
  • Further Reading
  • 10.5.
  • Exercises
  • 11.
  • Graph Pattern Mining
  • 11.1.
  • Isomorphism and Support
  • 11.2.
  • Further Reading
  • Candidate Generation
  • 11.3.
  • The gSpan Algorithm
  • 11.4.
  • Further Reading
  • 11.5.
  • Exercises
  • 12.
  • Pattern and Rule Assessment
  • 12.1.
  • 1.7.
  • Rule and Pattern Assessment Measures
  • 12.2.
  • Significance Testing and Confidence Intervals
  • 12.3.
  • Further Reading
  • 12.4.
  • Exercises
  • pt. THREE
  • CLUSTERING
  • 13.
  • Exercises
  • Representative-based Clustering
  • 13.1.
  • K-means Algorithm
  • 13.2.
  • Kernel K-means
  • 13.3.
  • Expectation-Maximization Clustering
  • 13.4.
  • Further Reading
  • 13.5.
  • pt. ONE
  • Exercises
  • 14.
  • Hierarchical Clustering
  • 14.1.
  • Preliminaries
  • 14.2.
  • Agglomerative Hierarchical Clustering
  • 14.3.
  • Further Reading
  • 14.4.
  • Machine generated contents note:
  • DATA ANALYSIS FOUNDATIONS
  • Exercises and Projects
  • 15.
  • Density-based Clustering
  • 15.1.
  • The DBSCAN Algorithm
  • 15.2.
  • Kernel Density Estimation
  • 15.3.
  • Density-based Clustering: DENCLUE
  • 15.4.
  • 2.
  • Further Reading
  • 15.5.
  • Exercises
  • 16.
  • Spectral and Graph Clustering
  • 16.1.
  • Graphs and Matrices
  • 16.2.
  • Clustering as Graph Cuts
  • 16.3.
  • Numeric Attributes
  • Markov Clustering
  • 16.4.
  • Further Reading
  • 16.5.
  • Exercises
  • 17.
  • Clustering Validation
  • 17.1.
  • External Measures
  • 17.2.
  • 2.1.
  • Internal Measures
  • 17.3.
  • Relative Measures
  • 17.4.
  • Further Reading
  • 17.5.
  • Exercises
  • pt. FOUR
  • CLASSIFICATION
  • 18.
  • Univariate Analysis
  • Probabilistic Classification
  • 18.1.
  • Bayes Classifier
  • 18.2.
  • Naive Bayes Classifier
  • 18.3.
  • K Nearest Neighbors Classifier
  • 18.4.
  • Further Reading
  • 18.5.
  • 2.2.
  • Exercises
  • 19.
  • Decision Tree Classifier
  • 19.1.
  • Decision Trees
  • 19.2.
  • Decision Tree Algorithm
  • 19.3.
  • Further Reading
  • 19.4.
  • Bivariate Analysis
  • Exercises
  • 20.
  • Linear Discriminant Analysis
  • 20.1.
  • Optimal Linear Discriminant
  • 20.2.
  • Kernel Discriminant Analysis
  • 20.3.
  • Further Reading
  • 20.4.
  • 2.3.
  • Exercises
  • 21.
  • Support Vector Machines
  • 21.1.
  • Support Vectors and Margins
  • 21.2.
  • SVM: Linear and Separable Case
  • 21.3.
  • Soft Margin SVM: Linear and Nonseparable Case
  • 21.4.
  • Multivariate Analysis
  • Kernel SVM: Nonlinear Case
  • 21.5.
  • SVM Training Algorithms
  • 21.6.
  • Further Reading
  • 21.7.
  • Exercises
  • 22.
  • Classification Assessment
  • 22.1.
  • 2.4.
  • Classification Performance Measures
  • 22.2.
  • Classifier Evaluation
  • 22.3.
  • Bias-Variance Decomposition
  • 22.4.
  • Further Reading
  • 22.5.
  • Exercises
  • 1.
  • Data Normalization
  • 2.5.
  • Normal Distribution
  • 2.6.
  • Further Reading
  • 2.7.
  • Exercises
  • 3.
  • Categorical Attributes
  • 3.1.
  • Data Mining and Analysis
  • Univariate Analysis
  • 3.2.
  • Bivariate Analysis
  • 3.3.
  • Multivariate Analysis
  • 3.4.
  • Distance and Angle
  • 3.5.
  • Discretization
  • 3.6.
  • 1.1.
  • Further Reading
  • 3.7.
  • Exercises
  • 4.
  • Graph Data
  • 4.1.
  • Graph Concepts
  • 4.2.
  • Topological Attributes
  • 4.3.
  • Data Matrix
  • Centrality Analysis
  • 4.4.
  • Graph Models
  • 4.5.
  • Further Reading
  • 4.6.
  • Exercises
  • 5.
  • Kernel Methods
  • 5.1.
  • 1.2.
  • Kernel Matrix
  • 5.2.
  • Vector Kernels
  • 5.3.
  • Basic Kernel Operations in Feature Space
  • 5.4.
  • Kernels for Complex Objects
  • 5.5.
  • Further Reading
  • 5.6.
  • Attributes
  • Exercises
  • 6.
  • High-dimensional Data
  • 6.1.
  • High-dimensional Objects
  • 6.2.
  • High-dimensional Volumes
  • 6.3.
  • Hypersphere Inscribed within Hypercube
  • 6.4.
  • 1.3.
  • Volume of Thin Hypersphere Shell
  • 6.5.
  • Diagonals in Hyperspace
  • 6.6.
  • Density of the Multivariate Normal
  • 6.7.
  • Appendix: Derivation of Hypersphere Volume
  • 6.8.
  • Further Reading
  • 6.9.
Control code
ocn858610961
Dimensions
27 cm
Extent
xi, 593 pages
Isbn
9780521766333
Isbn Type
(hardback : alk. paper)
Lccn
2013037544
Media category
unmediated
Media MARC source
rdamedia
System control number
(OCoLC)858610961

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