Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (XXXIII, 629 S.) : , Ill., graph. Darst.

Edition: 3. ed.

ISBN: 978-0-12-374856-0

Series Statement: The Morgan Kaufmann series in data management systems

Language: English

Subjects: Computer Science , Economics , Mathematics , Psychology

Keywords: Data Mining ; Maschinelles Lernen ; Weka 3 ; Data Mining ; Data Mining ; Java

URL: Volltext  (An electronic book accessible through the World Wide Web; click for information)

Author information: Frank, Eibe.

Author information: Witten, Ian H., 1947-

UID:

Format: 1 Online-Ressource (xxxii, 621 Seiten) : , Illustrationen, Diagramme.

Edition: Fourth edition

ISBN: 978-0-12-804357-8

Note: ISBN der Druckausgabe wird auf Webseite fälschlicherweise auch als ISBN für das E-Book angegeben.

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-804291-5

Language: English

Subjects: Computer Science , Economics , Psychology , Mathematics , Sociology

Keywords: Data Mining ; Maschinelles Lernen ; Weka 3 ; Data Mining ; Data Mining ; Java

URL: https://www.sciencedirect.com/book/9780128042915

URL: https://www.sciencedirect.com/book/9780128042915

Author information: Witten, Ian H., 1947-,

Author information: Frank, Eibe

UID:

Format: 1 online resource (655 pages)

Edition: 4th ed.

ISBN: 9780128043578

Series Statement: Morgan Kaufmann Series in Data Management Systems

Note: Front Cover -- Data Mining -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- Updated and Revised Content -- Second Edition -- Third Edition -- Fourth Edition -- Acknowledgments -- I. Introduction to data mining -- 1 What's it all about? -- 1.1 Data Mining and Machine Learning -- Describing Structural Patterns -- Machine Learning -- Data Mining -- 1.2 Simple Examples: The Weather Problem and Others -- The Weather Problem -- Contact Lenses: An Idealized Problem -- Irises: A Classic Numeric Dataset -- CPU Performance: Introducing Numeric Prediction -- Labor Negotiations: A More Realistic Example -- Soybean Classification: A Classic Machine Learning Success -- 1.3 Fielded Applications -- Web Mining -- Decisions Involving Judgment -- Screening Images -- Load Forecasting -- Diagnosis -- Marketing and Sales -- Other Applications -- 1.4 The Data Mining Process -- 1.5 Machine Learning and Statistics -- 1.6 Generalization as Search -- Enumerating the Concept Space -- Bias -- Language bias -- Search bias -- Overfitting-avoidance bias -- 1.7 Data Mining and Ethics -- Reidentification -- Using Personal Information -- Wider Issues -- 1.8 Further Reading and Bibliographic Notes -- 2 Input: concepts, instances, attributes -- 2.1 What's a Concept? -- 2.2 What's in an Example? -- Relations -- Other Example Types -- 2.3 What's in an Attribute? -- 2.4 Preparing the Input -- Gathering the Data Together -- ARFF Format -- Sparse Data -- Attribute Types -- Missing Values -- Inaccurate Values -- Unbalanced Data -- Getting to Know Your Data -- 2.5 Further Reading and Bibliographic Notes -- 3 Output: knowledge representation -- 3.1 Tables -- 3.2 Linear Models -- 3.3 Trees -- 3.4 Rules -- Classification Rules -- Association Rules -- Rules With Exceptions -- More Expressive Rules -- 3.5 Instance-Based Representation -- 3.6 Clusters. , 3.7 Further Reading and Bibliographic Notes -- 4 Algorithms: the basic methods -- 4.1 Inferring Rudimentary Rules -- Missing Values and Numeric Attributes -- 4.2 Simple Probabilistic Modeling -- Missing Values and Numeric Attributes -- Naïve Bayes for Document Classification -- Remarks -- 4.3 Divide-and-Conquer: Constructing Decision Trees -- Calculating Information -- Highly Branching Attributes -- 4.4 Covering Algorithms: Constructing Rules -- Rules Versus Trees -- A Simple Covering Algorithm -- Rules Versus Decision Lists -- 4.5 Mining Association Rules -- Item Sets -- Association Rules -- Generating Rules Efficiently -- 4.6 Linear Models -- Numeric Prediction: Linear Regression -- Linear Classification: Logistic Regression -- Linear Classification Using the Perceptron -- Linear Classification Using Winnow -- 4.7 Instance-Based Learning -- The Distance Function -- Finding Nearest Neighbors Efficiently -- Remarks -- 4.8 Clustering -- Iterative Distance-Based Clustering -- Faster Distance Calculations -- Choosing the Number of Clusters -- Hierarchical Clustering -- Example of Hierarchical Clustering -- Incremental Clustering -- Category Utility -- Remarks -- 4.9 Multi-instance Learning -- Aggregating the Input -- Aggregating the Output -- 4.10 Further Reading and Bibliographic Notes -- 4.11 Weka Implementations -- 5 Credibility: evaluating what's been learned -- 5.1 Training and Testing -- 5.2 Predicting Performance -- 5.3 Cross-Validation -- 5.4 Other Estimates -- Leave-One-Out -- The Bootstrap -- 5.5 Hyperparameter Selection -- 5.6 Comparing Data Mining Schemes -- 5.7 Predicting Probabilities -- Quadratic Loss Function -- Informational Loss Function -- Remarks -- 5.8 Counting the Cost -- Cost-Sensitive Classification -- Cost-Sensitive Learning -- Lift Charts -- ROC Curves -- Recall-Precision Curves -- Remarks -- Cost Curves. , 5.9 Evaluating Numeric Prediction -- 5.10 The MDL Principle -- 5.11 Applying the MDL Principle to Clustering -- 5.12 Using a Validation Set for Model Selection -- 5.13 Further Reading and Bibliographic Notes -- II. More advanced machine learning schemes -- 6 Trees and rules -- 6.1 Decision Trees -- Numeric Attributes -- Missing Values -- Pruning -- Estimating Error Rates -- Complexity of Decision Tree Induction -- From Trees to Rules -- C4.5: Choices and Options -- Cost-Complexity Pruning -- Discussion -- 6.2 Classification Rules -- Criteria for Choosing Tests -- Missing Values, Numeric Attributes -- Generating Good Rules -- Using Global Optimization -- Obtaining Rules From Partial Decision Trees -- Rules With Exceptions -- Discussion -- 6.3 Association Rules -- Building a Frequent Pattern Tree -- Finding Large Item Sets -- Discussion -- 6.4 Weka Implementations -- 7 Extending instance-based and linear models -- 7.1 Instance-Based Learning -- Reducing the Number of Exemplars -- Pruning Noisy Exemplars -- Weighting Attributes -- Generalizing Exemplars -- Distance Functions for Generalized Exemplars -- Generalized Distance Functions -- Discussion -- 7.2 Extending Linear Models -- The Maximum Margin Hyperplane -- Nonlinear Class Boundaries -- Support Vector Regression -- Kernel Ridge Regression -- The Kernel Perceptron -- Multilayer Perceptrons -- Backpropagation -- Radial Basis Function Networks -- Stochastic Gradient Descent -- Discussion -- 7.3 Numeric Prediction With Local Linear Models -- Model Trees -- Building the Tree -- Pruning the Tree -- Nominal Attributes -- Missing Values -- Pseudocode for Model Tree Induction -- Rules From Model Trees -- Locally Weighted Linear Regression -- Discussion -- 7.4 Weka Implementations -- 8 Data transformations -- 8.1 Attribute Selection -- Scheme-Independent Selection -- Searching the Attribute Space. , Scheme-Specific Selection -- 8.2 Discretizing Numeric Attributes -- Unsupervised Discretization -- Entropy-Based Discretization -- Other Discretization Methods -- Entropy-Based Versus Error-Based Discretization -- Converting Discrete to Numeric Attributes -- 8.3 Projections -- Principal Component Analysis -- Random Projections -- Partial Least Squares Regression -- Independent Component Analysis -- Linear Discriminant Analysis -- Quadratic Discriminant Analysis -- Fisher's Linear Discriminant Analysis -- Text to Attribute Vectors -- Time Series -- 8.4 Sampling -- Reservoir Sampling -- 8.5 Cleansing -- Improving Decision Trees -- Robust Regression -- Detecting Anomalies -- One-Class Learning -- Outlier Detection -- Generating Artificial Data -- 8.6 Transforming Multiple Classes to Binary Ones -- Simple Methods -- Error-Correcting Output Codes -- Ensembles of Nested Dichotomies -- 8.7 Calibrating Class Probabilities -- 8.8 Further Reading and Bibliographic Notes -- 8.9 Weka Implementations -- 9 Probabilistic methods -- 9.1 Foundations -- Maximum Likelihood Estimation -- Maximum a Posteriori Parameter Estimation -- 9.2 Bayesian Networks -- Making Predictions -- Learning Bayesian Networks -- Specific Algorithms -- Data Structures for Fast Learning -- 9.3 Clustering and Probability Density Estimation -- The Expectation Maximization Algorithm for a Mixture of Gaussians -- Extending the Mixture Model -- Clustering Using Prior Distributions -- Clustering With Correlated Attributes -- Kernel Density Estimation -- Comparing Parametric, Semiparametric and Nonparametric Density Models for Classification -- 9.4 Hidden Variable Models -- Expected Log-Likelihoods and Expected Gradients -- The Expectation Maximization Algorithm -- Applying the Expectation Maximization Algorithm to Bayesian Networks -- 9.5 Bayesian Estimation and Prediction. , Probabilistic Inference Methods -- Probability propagation -- Sampling, simulated annealing, and iterated conditional modes -- Variational inference -- 9.6 Graphical Models and Factor Graphs -- Graphical Models and Plate Notation -- Probabilistic Principal Component Analysis -- Inference with PPCA -- Marginal log-likelihood for PPCA -- Expected log-likelihood for PPCA -- Expected gradient for PPCA -- EM for PPCA -- Latent Semantic Analysis -- Using Principal Component Analysis for Dimensionality Reduction -- Probabilistic LSA -- Latent Dirichlet Allocation -- Factor Graphs -- Factor graphs, Bayesian networks, and the logistic regression model -- Markov Random Fields -- Computing Using the Sum-Product and Max-Product Algorithms -- Marginal probabilities -- The sum-product algorithm -- Sum-product algorithm example -- Most probable explanation example -- The max-product or max-sum algorithm -- 9.7 Conditional Probability Models -- Linear and Polynomial Regression as Probability Models -- Using Priors on Parameters -- Matrix vector formulations of linear and polynomial regression -- Multiclass Logistic Regression -- Matrix vector formulation of multiclass logistic regression -- Priors on parameters, and the regularized loss function -- Gradient Descent and Second-Order Methods -- Generalized Linear Models -- Making Predictions for Ordered Classes -- Conditional Probabilistic Models Using Kernels -- 9.8 Sequential and Temporal Models -- Markov Models and N-gram Methods -- Hidden Markov Models -- Conditional Random Fields -- From Markov random fields to conditional random fields -- Linear chain conditional random fields -- Learning for chain-structured conditional random fields -- Using conditional random fields for text mining -- 9.9 Further Reading and Bibliographic Notes -- Software Packages and Implementations -- 9.10 Weka Implementations. , 10 Deep learning.

Additional Edition: Print version: Witten, Ian H. Data Mining San Francisco : Elsevier Science & Technology,c2016 ISBN 9780128042915

Language: English

UID:

Format: 1 online resource (655 pages)

Edition: 4th ed.

ISBN: 9780128043578

Series Statement: Morgan Kaufmann Series in Data Management Systems

Note: Front Cover -- Data Mining -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- Updated and Revised Content -- Second Edition -- Third Edition -- Fourth Edition -- Acknowledgments -- I. Introduction to data mining -- 1 What's it all about? -- 1.1 Data Mining and Machine Learning -- Describing Structural Patterns -- Machine Learning -- Data Mining -- 1.2 Simple Examples: The Weather Problem and Others -- The Weather Problem -- Contact Lenses: An Idealized Problem -- Irises: A Classic Numeric Dataset -- CPU Performance: Introducing Numeric Prediction -- Labor Negotiations: A More Realistic Example -- Soybean Classification: A Classic Machine Learning Success -- 1.3 Fielded Applications -- Web Mining -- Decisions Involving Judgment -- Screening Images -- Load Forecasting -- Diagnosis -- Marketing and Sales -- Other Applications -- 1.4 The Data Mining Process -- 1.5 Machine Learning and Statistics -- 1.6 Generalization as Search -- Enumerating the Concept Space -- Bias -- Language bias -- Search bias -- Overfitting-avoidance bias -- 1.7 Data Mining and Ethics -- Reidentification -- Using Personal Information -- Wider Issues -- 1.8 Further Reading and Bibliographic Notes -- 2 Input: concepts, instances, attributes -- 2.1 What's a Concept? -- 2.2 What's in an Example? -- Relations -- Other Example Types -- 2.3 What's in an Attribute? -- 2.4 Preparing the Input -- Gathering the Data Together -- ARFF Format -- Sparse Data -- Attribute Types -- Missing Values -- Inaccurate Values -- Unbalanced Data -- Getting to Know Your Data -- 2.5 Further Reading and Bibliographic Notes -- 3 Output: knowledge representation -- 3.1 Tables -- 3.2 Linear Models -- 3.3 Trees -- 3.4 Rules -- Classification Rules -- Association Rules -- Rules With Exceptions -- More Expressive Rules -- 3.5 Instance-Based Representation -- 3.6 Clusters. , 3.7 Further Reading and Bibliographic Notes -- 4 Algorithms: the basic methods -- 4.1 Inferring Rudimentary Rules -- Missing Values and Numeric Attributes -- 4.2 Simple Probabilistic Modeling -- Missing Values and Numeric Attributes -- Naïve Bayes for Document Classification -- Remarks -- 4.3 Divide-and-Conquer: Constructing Decision Trees -- Calculating Information -- Highly Branching Attributes -- 4.4 Covering Algorithms: Constructing Rules -- Rules Versus Trees -- A Simple Covering Algorithm -- Rules Versus Decision Lists -- 4.5 Mining Association Rules -- Item Sets -- Association Rules -- Generating Rules Efficiently -- 4.6 Linear Models -- Numeric Prediction: Linear Regression -- Linear Classification: Logistic Regression -- Linear Classification Using the Perceptron -- Linear Classification Using Winnow -- 4.7 Instance-Based Learning -- The Distance Function -- Finding Nearest Neighbors Efficiently -- Remarks -- 4.8 Clustering -- Iterative Distance-Based Clustering -- Faster Distance Calculations -- Choosing the Number of Clusters -- Hierarchical Clustering -- Example of Hierarchical Clustering -- Incremental Clustering -- Category Utility -- Remarks -- 4.9 Multi-instance Learning -- Aggregating the Input -- Aggregating the Output -- 4.10 Further Reading and Bibliographic Notes -- 4.11 Weka Implementations -- 5 Credibility: evaluating what's been learned -- 5.1 Training and Testing -- 5.2 Predicting Performance -- 5.3 Cross-Validation -- 5.4 Other Estimates -- Leave-One-Out -- The Bootstrap -- 5.5 Hyperparameter Selection -- 5.6 Comparing Data Mining Schemes -- 5.7 Predicting Probabilities -- Quadratic Loss Function -- Informational Loss Function -- Remarks -- 5.8 Counting the Cost -- Cost-Sensitive Classification -- Cost-Sensitive Learning -- Lift Charts -- ROC Curves -- Recall-Precision Curves -- Remarks -- Cost Curves. , 5.9 Evaluating Numeric Prediction -- 5.10 The MDL Principle -- 5.11 Applying the MDL Principle to Clustering -- 5.12 Using a Validation Set for Model Selection -- 5.13 Further Reading and Bibliographic Notes -- II. More advanced machine learning schemes -- 6 Trees and rules -- 6.1 Decision Trees -- Numeric Attributes -- Missing Values -- Pruning -- Estimating Error Rates -- Complexity of Decision Tree Induction -- From Trees to Rules -- C4.5: Choices and Options -- Cost-Complexity Pruning -- Discussion -- 6.2 Classification Rules -- Criteria for Choosing Tests -- Missing Values, Numeric Attributes -- Generating Good Rules -- Using Global Optimization -- Obtaining Rules From Partial Decision Trees -- Rules With Exceptions -- Discussion -- 6.3 Association Rules -- Building a Frequent Pattern Tree -- Finding Large Item Sets -- Discussion -- 6.4 Weka Implementations -- 7 Extending instance-based and linear models -- 7.1 Instance-Based Learning -- Reducing the Number of Exemplars -- Pruning Noisy Exemplars -- Weighting Attributes -- Generalizing Exemplars -- Distance Functions for Generalized Exemplars -- Generalized Distance Functions -- Discussion -- 7.2 Extending Linear Models -- The Maximum Margin Hyperplane -- Nonlinear Class Boundaries -- Support Vector Regression -- Kernel Ridge Regression -- The Kernel Perceptron -- Multilayer Perceptrons -- Backpropagation -- Radial Basis Function Networks -- Stochastic Gradient Descent -- Discussion -- 7.3 Numeric Prediction With Local Linear Models -- Model Trees -- Building the Tree -- Pruning the Tree -- Nominal Attributes -- Missing Values -- Pseudocode for Model Tree Induction -- Rules From Model Trees -- Locally Weighted Linear Regression -- Discussion -- 7.4 Weka Implementations -- 8 Data transformations -- 8.1 Attribute Selection -- Scheme-Independent Selection -- Searching the Attribute Space. , Scheme-Specific Selection -- 8.2 Discretizing Numeric Attributes -- Unsupervised Discretization -- Entropy-Based Discretization -- Other Discretization Methods -- Entropy-Based Versus Error-Based Discretization -- Converting Discrete to Numeric Attributes -- 8.3 Projections -- Principal Component Analysis -- Random Projections -- Partial Least Squares Regression -- Independent Component Analysis -- Linear Discriminant Analysis -- Quadratic Discriminant Analysis -- Fisher's Linear Discriminant Analysis -- Text to Attribute Vectors -- Time Series -- 8.4 Sampling -- Reservoir Sampling -- 8.5 Cleansing -- Improving Decision Trees -- Robust Regression -- Detecting Anomalies -- One-Class Learning -- Outlier Detection -- Generating Artificial Data -- 8.6 Transforming Multiple Classes to Binary Ones -- Simple Methods -- Error-Correcting Output Codes -- Ensembles of Nested Dichotomies -- 8.7 Calibrating Class Probabilities -- 8.8 Further Reading and Bibliographic Notes -- 8.9 Weka Implementations -- 9 Probabilistic methods -- 9.1 Foundations -- Maximum Likelihood Estimation -- Maximum a Posteriori Parameter Estimation -- 9.2 Bayesian Networks -- Making Predictions -- Learning Bayesian Networks -- Specific Algorithms -- Data Structures for Fast Learning -- 9.3 Clustering and Probability Density Estimation -- The Expectation Maximization Algorithm for a Mixture of Gaussians -- Extending the Mixture Model -- Clustering Using Prior Distributions -- Clustering With Correlated Attributes -- Kernel Density Estimation -- Comparing Parametric, Semiparametric and Nonparametric Density Models for Classification -- 9.4 Hidden Variable Models -- Expected Log-Likelihoods and Expected Gradients -- The Expectation Maximization Algorithm -- Applying the Expectation Maximization Algorithm to Bayesian Networks -- 9.5 Bayesian Estimation and Prediction. , Probabilistic Inference Methods -- Probability propagation -- Sampling, simulated annealing, and iterated conditional modes -- Variational inference -- 9.6 Graphical Models and Factor Graphs -- Graphical Models and Plate Notation -- Probabilistic Principal Component Analysis -- Inference with PPCA -- Marginal log-likelihood for PPCA -- Expected log-likelihood for PPCA -- Expected gradient for PPCA -- EM for PPCA -- Latent Semantic Analysis -- Using Principal Component Analysis for Dimensionality Reduction -- Probabilistic LSA -- Latent Dirichlet Allocation -- Factor Graphs -- Factor graphs, Bayesian networks, and the logistic regression model -- Markov Random Fields -- Computing Using the Sum-Product and Max-Product Algorithms -- Marginal probabilities -- The sum-product algorithm -- Sum-product algorithm example -- Most probable explanation example -- The max-product or max-sum algorithm -- 9.7 Conditional Probability Models -- Linear and Polynomial Regression as Probability Models -- Using Priors on Parameters -- Matrix vector formulations of linear and polynomial regression -- Multiclass Logistic Regression -- Matrix vector formulation of multiclass logistic regression -- Priors on parameters, and the regularized loss function -- Gradient Descent and Second-Order Methods -- Generalized Linear Models -- Making Predictions for Ordered Classes -- Conditional Probabilistic Models Using Kernels -- 9.8 Sequential and Temporal Models -- Markov Models and N-gram Methods -- Hidden Markov Models -- Conditional Random Fields -- From Markov random fields to conditional random fields -- Linear chain conditional random fields -- Learning for chain-structured conditional random fields -- Using conditional random fields for text mining -- 9.9 Further Reading and Bibliographic Notes -- Software Packages and Implementations -- 9.10 Weka Implementations. , 10 Deep learning.

Additional Edition: Print version: Witten, Ian H. Data Mining San Francisco : Elsevier Science & Technology,c2016 ISBN 9780128042915

Language: English

UID:

Format: 1 online resource (655 pages)

Edition: 4th ed.

ISBN: 9780128043578

Series Statement: Morgan Kaufmann Series in Data Management Systems

Note: Front Cover -- Data Mining -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- Updated and Revised Content -- Second Edition -- Third Edition -- Fourth Edition -- Acknowledgments -- I. Introduction to data mining -- 1 What's it all about? -- 1.1 Data Mining and Machine Learning -- Describing Structural Patterns -- Machine Learning -- Data Mining -- 1.2 Simple Examples: The Weather Problem and Others -- The Weather Problem -- Contact Lenses: An Idealized Problem -- Irises: A Classic Numeric Dataset -- CPU Performance: Introducing Numeric Prediction -- Labor Negotiations: A More Realistic Example -- Soybean Classification: A Classic Machine Learning Success -- 1.3 Fielded Applications -- Web Mining -- Decisions Involving Judgment -- Screening Images -- Load Forecasting -- Diagnosis -- Marketing and Sales -- Other Applications -- 1.4 The Data Mining Process -- 1.5 Machine Learning and Statistics -- 1.6 Generalization as Search -- Enumerating the Concept Space -- Bias -- Language bias -- Search bias -- Overfitting-avoidance bias -- 1.7 Data Mining and Ethics -- Reidentification -- Using Personal Information -- Wider Issues -- 1.8 Further Reading and Bibliographic Notes -- 2 Input: concepts, instances, attributes -- 2.1 What's a Concept? -- 2.2 What's in an Example? -- Relations -- Other Example Types -- 2.3 What's in an Attribute? -- 2.4 Preparing the Input -- Gathering the Data Together -- ARFF Format -- Sparse Data -- Attribute Types -- Missing Values -- Inaccurate Values -- Unbalanced Data -- Getting to Know Your Data -- 2.5 Further Reading and Bibliographic Notes -- 3 Output: knowledge representation -- 3.1 Tables -- 3.2 Linear Models -- 3.3 Trees -- 3.4 Rules -- Classification Rules -- Association Rules -- Rules With Exceptions -- More Expressive Rules -- 3.5 Instance-Based Representation -- 3.6 Clusters. , 3.7 Further Reading and Bibliographic Notes -- 4 Algorithms: the basic methods -- 4.1 Inferring Rudimentary Rules -- Missing Values and Numeric Attributes -- 4.2 Simple Probabilistic Modeling -- Missing Values and Numeric Attributes -- Naïve Bayes for Document Classification -- Remarks -- 4.3 Divide-and-Conquer: Constructing Decision Trees -- Calculating Information -- Highly Branching Attributes -- 4.4 Covering Algorithms: Constructing Rules -- Rules Versus Trees -- A Simple Covering Algorithm -- Rules Versus Decision Lists -- 4.5 Mining Association Rules -- Item Sets -- Association Rules -- Generating Rules Efficiently -- 4.6 Linear Models -- Numeric Prediction: Linear Regression -- Linear Classification: Logistic Regression -- Linear Classification Using the Perceptron -- Linear Classification Using Winnow -- 4.7 Instance-Based Learning -- The Distance Function -- Finding Nearest Neighbors Efficiently -- Remarks -- 4.8 Clustering -- Iterative Distance-Based Clustering -- Faster Distance Calculations -- Choosing the Number of Clusters -- Hierarchical Clustering -- Example of Hierarchical Clustering -- Incremental Clustering -- Category Utility -- Remarks -- 4.9 Multi-instance Learning -- Aggregating the Input -- Aggregating the Output -- 4.10 Further Reading and Bibliographic Notes -- 4.11 Weka Implementations -- 5 Credibility: evaluating what's been learned -- 5.1 Training and Testing -- 5.2 Predicting Performance -- 5.3 Cross-Validation -- 5.4 Other Estimates -- Leave-One-Out -- The Bootstrap -- 5.5 Hyperparameter Selection -- 5.6 Comparing Data Mining Schemes -- 5.7 Predicting Probabilities -- Quadratic Loss Function -- Informational Loss Function -- Remarks -- 5.8 Counting the Cost -- Cost-Sensitive Classification -- Cost-Sensitive Learning -- Lift Charts -- ROC Curves -- Recall-Precision Curves -- Remarks -- Cost Curves. , 5.9 Evaluating Numeric Prediction -- 5.10 The MDL Principle -- 5.11 Applying the MDL Principle to Clustering -- 5.12 Using a Validation Set for Model Selection -- 5.13 Further Reading and Bibliographic Notes -- II. More advanced machine learning schemes -- 6 Trees and rules -- 6.1 Decision Trees -- Numeric Attributes -- Missing Values -- Pruning -- Estimating Error Rates -- Complexity of Decision Tree Induction -- From Trees to Rules -- C4.5: Choices and Options -- Cost-Complexity Pruning -- Discussion -- 6.2 Classification Rules -- Criteria for Choosing Tests -- Missing Values, Numeric Attributes -- Generating Good Rules -- Using Global Optimization -- Obtaining Rules From Partial Decision Trees -- Rules With Exceptions -- Discussion -- 6.3 Association Rules -- Building a Frequent Pattern Tree -- Finding Large Item Sets -- Discussion -- 6.4 Weka Implementations -- 7 Extending instance-based and linear models -- 7.1 Instance-Based Learning -- Reducing the Number of Exemplars -- Pruning Noisy Exemplars -- Weighting Attributes -- Generalizing Exemplars -- Distance Functions for Generalized Exemplars -- Generalized Distance Functions -- Discussion -- 7.2 Extending Linear Models -- The Maximum Margin Hyperplane -- Nonlinear Class Boundaries -- Support Vector Regression -- Kernel Ridge Regression -- The Kernel Perceptron -- Multilayer Perceptrons -- Backpropagation -- Radial Basis Function Networks -- Stochastic Gradient Descent -- Discussion -- 7.3 Numeric Prediction With Local Linear Models -- Model Trees -- Building the Tree -- Pruning the Tree -- Nominal Attributes -- Missing Values -- Pseudocode for Model Tree Induction -- Rules From Model Trees -- Locally Weighted Linear Regression -- Discussion -- 7.4 Weka Implementations -- 8 Data transformations -- 8.1 Attribute Selection -- Scheme-Independent Selection -- Searching the Attribute Space. , Scheme-Specific Selection -- 8.2 Discretizing Numeric Attributes -- Unsupervised Discretization -- Entropy-Based Discretization -- Other Discretization Methods -- Entropy-Based Versus Error-Based Discretization -- Converting Discrete to Numeric Attributes -- 8.3 Projections -- Principal Component Analysis -- Random Projections -- Partial Least Squares Regression -- Independent Component Analysis -- Linear Discriminant Analysis -- Quadratic Discriminant Analysis -- Fisher's Linear Discriminant Analysis -- Text to Attribute Vectors -- Time Series -- 8.4 Sampling -- Reservoir Sampling -- 8.5 Cleansing -- Improving Decision Trees -- Robust Regression -- Detecting Anomalies -- One-Class Learning -- Outlier Detection -- Generating Artificial Data -- 8.6 Transforming Multiple Classes to Binary Ones -- Simple Methods -- Error-Correcting Output Codes -- Ensembles of Nested Dichotomies -- 8.7 Calibrating Class Probabilities -- 8.8 Further Reading and Bibliographic Notes -- 8.9 Weka Implementations -- 9 Probabilistic methods -- 9.1 Foundations -- Maximum Likelihood Estimation -- Maximum a Posteriori Parameter Estimation -- 9.2 Bayesian Networks -- Making Predictions -- Learning Bayesian Networks -- Specific Algorithms -- Data Structures for Fast Learning -- 9.3 Clustering and Probability Density Estimation -- The Expectation Maximization Algorithm for a Mixture of Gaussians -- Extending the Mixture Model -- Clustering Using Prior Distributions -- Clustering With Correlated Attributes -- Kernel Density Estimation -- Comparing Parametric, Semiparametric and Nonparametric Density Models for Classification -- 9.4 Hidden Variable Models -- Expected Log-Likelihoods and Expected Gradients -- The Expectation Maximization Algorithm -- Applying the Expectation Maximization Algorithm to Bayesian Networks -- 9.5 Bayesian Estimation and Prediction. , Probabilistic Inference Methods -- Probability propagation -- Sampling, simulated annealing, and iterated conditional modes -- Variational inference -- 9.6 Graphical Models and Factor Graphs -- Graphical Models and Plate Notation -- Probabilistic Principal Component Analysis -- Inference with PPCA -- Marginal log-likelihood for PPCA -- Expected log-likelihood for PPCA -- Expected gradient for PPCA -- EM for PPCA -- Latent Semantic Analysis -- Using Principal Component Analysis for Dimensionality Reduction -- Probabilistic LSA -- Latent Dirichlet Allocation -- Factor Graphs -- Factor graphs, Bayesian networks, and the logistic regression model -- Markov Random Fields -- Computing Using the Sum-Product and Max-Product Algorithms -- Marginal probabilities -- The sum-product algorithm -- Sum-product algorithm example -- Most probable explanation example -- The max-product or max-sum algorithm -- 9.7 Conditional Probability Models -- Linear and Polynomial Regression as Probability Models -- Using Priors on Parameters -- Matrix vector formulations of linear and polynomial regression -- Multiclass Logistic Regression -- Matrix vector formulation of multiclass logistic regression -- Priors on parameters, and the regularized loss function -- Gradient Descent and Second-Order Methods -- Generalized Linear Models -- Making Predictions for Ordered Classes -- Conditional Probabilistic Models Using Kernels -- 9.8 Sequential and Temporal Models -- Markov Models and N-gram Methods -- Hidden Markov Models -- Conditional Random Fields -- From Markov random fields to conditional random fields -- Linear chain conditional random fields -- Learning for chain-structured conditional random fields -- Using conditional random fields for text mining -- 9.9 Further Reading and Bibliographic Notes -- Software Packages and Implementations -- 9.10 Weka Implementations. , 10 Deep learning.

Additional Edition: Print version: Witten, Ian H. Data Mining San Francisco : Elsevier Science & Technology,c2016 ISBN 9780128042915

Language: English

UID:

Format: xxxi, 525 p. : , ill.

Edition: 2nd ed.

Edition: Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.

Series Statement: Morgan Kaufmann series in data management systems

Language: English

Keywords: Electronic books.

URL: Click to View

UID:

Format: xxxiii, 629 p. : , ill.

Edition: 3rd ed.

Edition: Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.

Note: Part I. Machine learning tools and techniques: 1. What's it all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.

Language: English

Keywords: Electronic books.

URL: Click to View

UID:

Format: 1 online resource (xxxi, 524 p.) : , ill.

Edition: 2nd ed.

ISBN: 9786611008062 , 0-08-047702-X , 9781423722442 , 1-281-00806-0

Series Statement: Morgan Kaufmann series in data management systems

Content: As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. Algorithmic methods at the heart of successful data mining including tried and true techniques as well as leading edge methods. Performance improvement techniques that work by transforming the input or output. Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in a new, interactive interface.

Note: PART I: MACHINE LEARNING TOOLS AND TECHNIQUES; 1 What's it all about?; 2 Input: Concepts, instances, and attributes; 3 Output: Knowledge representation; 4 Algorithms: The basic methods; 5 Credibility: Evaluating what's been learned; 6 Implementations: Real machine learning schemes; 7 Transformations: Engineering the input and output; 8 Moving on: Extensions and applications; PART II: THE WEKA MACHINE LEARNING WORKBENCH; 9 Introduction to Weka; 10 The Explorer; 11 The Knowledge Flow Interface; 12 The Experimenter; 13 The Command-Line Interface; 14 Embedded machine learning; 15 Writing New Learning Schemes; References; Index; About the Authors.

Additional Edition: ISBN 0-12-088407-0

Additional Edition: ISBN 1-4237-2244-2

Language: English

UID:

Format: 1 Online-Ressource (XXXIII, 629 Seiten) : , Illustrationen, Diagramme.

Edition: Third edition

ISBN: 978-0-08-089036-4 , 0-08-089036-9 , 9780123748560

Series Statement: Morgan Kaufmann series in data management systems

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-374856-0

Language: English

Subjects: Computer Science , Economics

Keywords: Data Mining ; Maschinelles Lernen ; Weka 3 ; Data Mining ; Data Mining ; Java

DOI: 10.1016/C2009-0-19715-5

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Witten, Ian H. 1947-

Author information: Frank, Eibe

UID:

Format: 1 Online-Ressource (XXXIII, 629 Seiten) : , Illustrationen, Diagramme.

Edition: Third edition

ISBN: 978-0-08-089036-4 , 0-08-089036-9 , 9780123748560

Series Statement: Morgan Kaufmann series in data management systems

Note: Includes bibliographical references and index

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-374856-0

Language: English

Subjects: Computer Science , Economics

Keywords: Data Mining ; Maschinelles Lernen ; Weka 3 ; Data Mining ; Data Mining ; Java

DOI: 10.1016/C2009-0-19715-5

URL: Volltext  (URL des Erstveröffentlichers)

Author information: Witten, Ian H. 1947-

Author information: Frank, Eibe