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  • 1
    Book
    Book
    Data mining : concepts, models, methods, and algorithms (2011)
    Hoboken, NJ : Wiley
    Details
    UID:
    b3kat_BV036806384
    Format: XVII, 534 S. , Ill., graph. Darst.
    Edition: 2. ed.
    ISBN: 9780470890455
    Note: Includes bibliographical references and index.
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-118-02914-5
    Additional Edition: Erscheint auch als Online-Ausgabe, PDF ISBN 978-1-118-02912-1
    Additional Edition: Erscheint auch als Online-Ausgabe, EPUB ISBN 978-1-118-02913-8
    Language: English
    Subjects: Computer Science
    RVK:
    ST 270
    RVK:
    ST 530
    Keywords: Data Mining ; Datenanalyse ; Datensammlung
    URL: Inhaltsverzeichnis
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    Data mining : concepts, models, methods, and algorithms (2011)
    [Piscataway, New Jersey] :IEEE Press ;
    Details
    UID:
    almafu_9959785273602883
    Format: 1 online resource (xvii, 534 pages) : , illustrations
    Edition: Second edition.
    ISBN: 9781118029145 , 1118029143 , 9781118029121 , 1118029127 , 9781118029138 , 1118029135 , 9786613239747 , 6613239747 , 9781283239745 , 1283239744 , 9780470544341 , 0470544341
    Content: This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
    Content: "Now updated--the systematic introductory guide to modern analysis of large data sets. As data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information for decision-making. This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies: Support Vector Machines (SVM)--developed based on statistical learning theory, they have a large potential for applications in predictive data mining; Kohonen Maps (Self-Organizing Maps - SOM)--one of very applicative neural-networks-based methodologies for descriptive data mining and multi-dimensional data visualizations; DBSCAN, BIRCH, and distributed DBSCAN clustering algorithms--representatives of an important class of density-based clustering methodologies; Bayesian Networks (BN) methodology often used for causality modeling; Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks; CART algorithm and Gini index in building decision trees; Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm; Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning; PageRank algorithm for mining and authority ranking of web pages; Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents; New sections on temporal, spatial, web, text, parallel, and distributed data mining. More emphasis on business, privacy, security, and legal aspects of data mining technologyThis text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here."--Publisher's description
    Note: Data-Mining Concepts -- , Preparing the Data -- , Data Reduction -- , Learning from Data -- , Statistical Methods -- , Decision Trees and Decision Rules -- , Artificial Neural Networks -- , Ensemble Learning -- , Cluster Analysis -- , Association Rules -- , Web Mining and Text Mining -- , Advances in Data Mining -- , Genetic Algorithms -- , Fuzzy sets and Fuzzy Logic -- , Visualization Methods -- , Appendix A -- , Appendix B: Data-Mining Applications.
    Additional Edition: Print version: Kantardzic, Mehmed. Data mining. [Piscataway, New Jersey] : IEEE Press ; Hoboken, NJ : Wiley, [2011] ISBN 9780470890455
    Language: English
    Keywords: Electronic books. ; Datensammlung ; Electronic books. ; Electronic books. ; Electronic books. ; Electronic books.
    DOI: 10.1002/9781118029145.
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118029145
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118029145
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118029145
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118029145
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    Online Access
    Open Access Request
    HU Berlin
    FU Berlin
  • 3
    Online Resource
    Online Resource
    Data mining : concepts, models, methods, and algorithms (2011)
    [Piscataway, New Jersey] :IEEE Press ;
    Details
    UID:
    edocfu_9959327329202883
    Format: 1 online resource (xvii, 534 pages) : , illustrations
    Edition: Second edition.
    ISBN: 9781118029145 , 1118029143 , 9781118029121 , 1118029127 , 9781118029138 , 1118029135 , 9786613239747 , 6613239747
    Content: This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
    Content: "Now updated--the systematic introductory guide to modern analysis of large data sets. As data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information for decision-making. This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies: Support Vector Machines (SVM)--developed based on statistical learning theory, they have a large potential for applications in predictive data mining; Kohonen Maps (Self-Organizing Maps - SOM)--one of very applicative neural-networks-based methodologies for descriptive data mining and multi-dimensional data visualizations; DBSCAN, BIRCH, and distributed DBSCAN clustering algorithms--representatives of an important class of density-based clustering methodologies; Bayesian Networks (BN) methodology often used for causality modeling; Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks; CART algorithm and Gini index in building decision trees; Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm; Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning; PageRank algorithm for mining and authority ranking of web pages; Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents; New sections on temporal, spatial, web, text, parallel, and distributed data mining. More emphasis on business, privacy, security, and legal aspects of data mining technologyThis text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here."--Publisher's description.
    Note: Data-Mining Concepts -- , Preparing the Data -- , Data Reduction -- , Learning from Data -- , Statistical Methods -- , Decision Trees and Decision Rules -- , Artificial Neural Networks -- , Ensemble Learning -- , Cluster Analysis -- , Association Rules -- , Web Mining and Text Mining -- , Advances in Data Mining -- , Genetic Algorithms -- , Fuzzy sets and Fuzzy Logic -- , Visualization Methods -- , Appendix A -- , Appendix B: Data-Mining Applications.
    Additional Edition: Print version: Kantardzic, Mehmed. Data mining. Hoboken, N.J. : John Wiley : IEEE Press, ©2011 ISBN 9780470890455
    Language: English
    Keywords: Electronic books.
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118029145
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    FU Berlin
  • 4
    Online Resource
    Online Resource
    Data mining : concepts, models, methods, and algorithms (2011)
    Hoboken, NJ :Wiley,
    Details
    UID:
    edocfu_BV041261126
    Format: 1 Online-Ressource (xvii, 534 Seiten) : , Illustrationen, Diagramme.
    Edition: Second edition
    ISBN: 978-1-118-02914-5 , 978-1-118-02912-1 , 978-1-118-02913-8 , 9781119516057
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-0-470-89045-5
    Language: English
    Subjects: Computer Science
    RVK:
    ST 270
    RVK:
    ST 530
    Keywords: Data Mining ; Datenanalyse ; Datensammlung
    DOI: 10.1002/9781118029145
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Online Access
    Open Access Request
    FU Berlin
  • 5
    Online Resource
    Online Resource
    Data mining : concepts, models, methods, and algorithms (2011)
    [Piscataway, New Jersey] :IEEE Press ;
    Details
    UID:
    edocfu_9959707703302883
    Format: 1 online resource (xvii, 534 pages) : , illustrations
    Edition: Second edition.
    ISBN: 9781118029145 , 1118029143 , 9781118029121 , 1118029127 , 9781118029138 , 1118029135 , 9786613239747 , 6613239747 , 9781283239745 , 1283239744
    Content: This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
    Content: "Now updated--the systematic introductory guide to modern analysis of large data sets. As data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information for decision-making. This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies: Support Vector Machines (SVM)--developed based on statistical learning theory, they have a large potential for applications in predictive data mining; Kohonen Maps (Self-Organizing Maps - SOM)--one of very applicative neural-networks-based methodologies for descriptive data mining and multi-dimensional data visualizations; DBSCAN, BIRCH, and distributed DBSCAN clustering algorithms--representatives of an important class of density-based clustering methodologies; Bayesian Networks (BN) methodology often used for causality modeling; Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks; CART algorithm and Gini index in building decision trees; Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm; Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning; PageRank algorithm for mining and authority ranking of web pages; Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents; New sections on temporal, spatial, web, text, parallel, and distributed data mining. More emphasis on business, privacy, security, and legal aspects of data mining technologyThis text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here."--Publisher's description
    Note: Data-Mining Concepts -- , Preparing the Data -- , Data Reduction -- , Learning from Data -- , Statistical Methods -- , Decision Trees and Decision Rules -- , Artificial Neural Networks -- , Ensemble Learning -- , Cluster Analysis -- , Association Rules -- , Web Mining and Text Mining -- , Advances in Data Mining -- , Genetic Algorithms -- , Fuzzy sets and Fuzzy Logic -- , Visualization Methods -- , Appendix A -- , Appendix B: Data-Mining Applications.
    Additional Edition: Print version: Kantardzic, Mehmed. Data mining. [Piscataway, New Jersey] : IEEE Press ; Hoboken, NJ : Wiley, [2011] ISBN 9780470890455
    Language: English
    Keywords: Electronic books.
    DOI: 10.1002/9781118029145.
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118029145
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    FU Berlin
  • 6
    Online Resource
    Online Resource
    Data mining : concepts, models, methods, and algorithms (2011)
    [Piscataway, New Jersey] :IEEE Press ;
    Details
    UID:
    edocfu_9959785273602883
    Format: 1 online resource (xvii, 534 pages) : , illustrations
    Edition: Second edition.
    ISBN: 9781118029145 , 1118029143 , 9781118029121 , 1118029127 , 9781118029138 , 1118029135 , 9786613239747 , 6613239747 , 9781283239745 , 1283239744 , 9780470544341 , 0470544341
    Content: This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
    Content: "Now updated--the systematic introductory guide to modern analysis of large data sets. As data sets continue to grow in size and complexity, there has been an inevitable move towards indirect, automatic, and intelligent data analysis in which the analyst works via more complex and sophisticated software tools. This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces to extract new information for decision-making. This Second Edition of Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples, and questions and exercises for practice at the end of each chapter. This new edition features the following new techniques/methodologies: Support Vector Machines (SVM)--developed based on statistical learning theory, they have a large potential for applications in predictive data mining; Kohonen Maps (Self-Organizing Maps - SOM)--one of very applicative neural-networks-based methodologies for descriptive data mining and multi-dimensional data visualizations; DBSCAN, BIRCH, and distributed DBSCAN clustering algorithms--representatives of an important class of density-based clustering methodologies; Bayesian Networks (BN) methodology often used for causality modeling; Algorithms for measuring Betweeness and Centrality parameters in graphs, important for applications in mining large social networks; CART algorithm and Gini index in building decision trees; Bagging & Boosting approaches to ensemble-learning methodologies, with details of AdaBoost algorithm; Relief algorithm, one of the core feature selection algorithms inspired by instance-based learning; PageRank algorithm for mining and authority ranking of web pages; Latent Semantic Analysis (LSA) for text mining and measuring semantic similarities between text-based documents; New sections on temporal, spatial, web, text, parallel, and distributed data mining. More emphasis on business, privacy, security, and legal aspects of data mining technologyThis text offers guidance on how and when to use a particular software tool (with the companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. The book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. This volume is primarily intended as a data-mining textbook for computer science, computer engineering, and computer information systems majors at the graduate level. Senior students at the undergraduate level and with the appropriate background can also successfully comprehend all topics presented here."--Publisher's description
    Note: Data-Mining Concepts -- , Preparing the Data -- , Data Reduction -- , Learning from Data -- , Statistical Methods -- , Decision Trees and Decision Rules -- , Artificial Neural Networks -- , Ensemble Learning -- , Cluster Analysis -- , Association Rules -- , Web Mining and Text Mining -- , Advances in Data Mining -- , Genetic Algorithms -- , Fuzzy sets and Fuzzy Logic -- , Visualization Methods -- , Appendix A -- , Appendix B: Data-Mining Applications.
    Additional Edition: Print version: Kantardzic, Mehmed. Data mining. [Piscataway, New Jersey] : IEEE Press ; Hoboken, NJ : Wiley, [2011] ISBN 9780470890455
    Language: English
    Keywords: Electronic books.
    DOI: 10.1002/9781118029145.
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118029145
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    FU Berlin
  • 7
    Online Resource
    Online Resource
    Data Mining : Concepts, Models, Methods, and Algorithms. (2011)
    Hoboken :Wiley,
    Details
    UID:
    almahu_9948203797802882
    Format: 1 online resource (554 pages)
    Edition: 2nd ed.
    ISBN: 0470890452 , 9780470890455 , 9781118029121 , 1118029127 , 1118029135 , 9781118029138 , 1118029143 , 9781118029145 , 9781119516057 , 1119516056
    Content: This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades. If you are an instructor or professor and would like to obtain instructor's materials, please visit 〈a href=""http://booksupport.wiley.c.
    Note: 10.4 IMPROVING THE EFFICIENCY OF THE APRIORI ALGORITHM. , DATA MINING Concepts, Models, Methods, and Algorithms, SECOND EDITION; CONTENTS; PREFACE TO THE SECOND EDITION; PREFACE TO THE FIRST EDITION; 1: DATA-MINING CONCEPTS; 1.1 INTRODUCTION; 1.2 DATA-MINING ROOTS; 1.3 DATA-MINING PROCESS; 1.4 LARGE DATA SETS; 1.5 DATA WAREHOUSES FOR DATA MINING; 1.6 BUSINESS ASPECTS OF DATA MINING: WHY A DATA-MINING PROJECT FAILS; 1.7 ORGANIZATION OF THIS BOOK; 1.8 REVIEW QUESTIONS AND PROBLEMS; 1.9 REFERENCES FOR FURTHER STUDY; 2: PREPARING THE DATA; 2.1 REPRESENTATION OF RAW DATA; 2.2 CHARACTERISTICS OF RAW DATA; 2.3 TRANSFORMATION OF RAW DATA; 2.4 MISSING DATA. , 2.5 TIME-DEPENDENT DATA2.6 OUTLIER ANALYSIS; 2.7 REVIEW QUESTIONS AND PROBLEMS; 2.8 REFERENCES FOR FURTHER STUDY; 3: DATA REDUCTION; 3.1 DIMENSIONS OF LARGE DATA SETS; 3.2 FEATURE REDUCTION; 3.3 RELIEF ALGORITHM; 3.4 ENTROPY MEASURE FOR RANKING FEATURES; 3.5 PCA; 3.6 VALUE REDUCTION; 3.7 FEATURE DISCRETIZATION: CHIMERGE TECHNIQUE; 3.8 CASE REDUCTION; 3.9 REVIEW QUESTIONS AND PROBLEMS; 3.10 REFERENCES FOR FURTHER STUDY; 4: LEARNING FROM DATA; 4.1 LEARNING MACHINE; 4.2 SLT; 4.3 TYPES OF LEARNING METHODS; 4.4 COMMON LEARNING TASKS; 4.5 SVMs; 4.6 KNN : NEAREST NEIGHBOR CLASSIFIER. , 4.7 MODEL SELECTION VERSUS GENERALIZATION4.8 MODEL ESTIMATION; 4.9 90% ACCURACY: NOW WHAT?; 4.10 REVIEW QUESTIONS AND PROBLEMS; 4.11 REFERENCES FOR FURTHER STUDY; 5: STATISTICAL METHODS; 5.1 STATISTICAL INFERENCE; 5.2 ASSESSING DIFFERENCES IN DATA SETS; 5.3 BAYESIAN INFERENCE; 5.4 PREDICTIVE REGRESSION; 5.5 ANOVA; 5.6 LOGISTIC REGRESSION; 5.7 LOG-LINEAR MODELS; 5.8 LDA; 5.9 REVIEW QUESTIONS AND PROBLEMS; 5.10 REFERENCES FOR FURTHER STUDY; 6: DECISION TREES AND DECISION RULES; 6.1 DECISION TREES; 6.2 C4.5 ALGORITHM: GENERATING A DECISION TREE; 6.3 UNKNOWN ATTRIBUTE VALUES. , 6.4 PRUNING DECISION TREES6.5 C4.5 ALGORITHM: GENERATING DECISION RULES; 6.6 CART ALGORITHM & GINI INDEX; 6.7 LIMITATIONS OF DECISION TREES AND DECISION RULES; 6.8 REVIEW QUESTIONS AND PROBLEMS; 6.9 REFERENCES FOR FURTHER STUDY; 7: ARTIFICIAL NEURAL NETWORKS; 7.1 MODEL OF AN ARTIFICIAL NEURON; 7.2 ARCHITECTURES OF ANNS; 7.3 LEARNING PROCESS; 7.4 LEARNING TASKS USING ANNS; 7.5 MULTILAYER PERCEPTRONS (MLPs); 7.6 COMPETITIVE NETWORKS AND COMPETITIVE LEARNING; 7.7 SOMs; 7.8 REVIEW QUESTIONS AND PROBLEMS; 7.9 REFERENCES FOR FURTHER STUDY; 8: ENSEMBLE LEARNING; 8.1 ENSEMBLE-LEARNING METHODOLOGIES. , 8.2 COMBINATION SCHEMES FOR MULTIPLE LEARNERS8.3 BAGGING AND BOOSTING; 8.4 ADABOOST; 8.5 REVIEW QUESTIONS AND PROBLEMS; 8.6 REFERENCES FOR FURTHER STUDY; 9: CLUSTER ANALYSIS; 9.1 CLUSTERING CONCEPTS; 9.2 SIMILARITY MEASURES; 9.3 AGGLOMERATIVE HIERARCHICAL CLUSTERING; 9.4 PARTITIONAL CLUSTERING; 9.5 INCREMENTAL CLUSTERING; 9.6 DBSCAN ALGORITHM; 9.7 BIRCH ALGORITHM; 9.8 CLUSTERING VALIDATION; 9.9 REVIEW QUESTIONS AND PROBLEMS; 9.10 REFERENCES FOR FURTHER STUDY; 10: ASSOCIATION RULES; 10.1 MARKET-BASKET ANALYSIS; 10.2 ALGORITHM APRIORI; 10.3 FROM FREQUENT ITEMSETS TO ASSOCIATION RULES. , English.
    Additional Edition: Print version: ISBN 9780470890455
    Language: English
    Keywords: Electronic books. ; Electronic book.
    URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119516057
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    HU Berlin
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