Kooperativer Bibliotheksverbund

UID:

Umfang: 1 online resource (718 pages) : , illustrations

ISBN: 9781118950845 (e-book)

Weitere Ausg.: Print version: Cichosz, Paweł. Data mining algorithms : explained using R. Chichester, England : Wiley, c2015 ISBN 9781118332580

Sprache: Englisch

Schlagwort(e): Electronic books.

URL: Click to View

UID:

Umfang: 1 online resource (xxxi, 683 pages)

ISBN: 9781118950845 , 1118950844 , 9781118950807 , 1118950801 , 9781322317465 , 1322317461

Inhalt: "This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

Anmerkung: Preliminaries -- , Tasks -- , Introduction -- , Knowledge -- , Inference -- , Inductive learning tasks -- , Domain -- , Instances -- , Attributes -- , Target attribute -- , Input attributes -- , Training set -- , Model -- , Performance -- , Generalization -- , Overfitting -- , Algorithms -- , Inductive learning as search -- , Classification -- , Concept -- , Training set -- , Model -- , Performance -- , Generalization -- , Overfitting -- , Algorithms -- , Regression -- , Target function -- , Training set -- , Model -- , Performance -- , Generalization -- , Overfitting -- , Algorithms -- , Clustering -- , Motivation -- , Training set -- , Model -- , Crisp vs. soft clustering -- , Hierarchical clustering -- , Performance -- , Generalization -- , Algorithms -- , Descriptive vs. predictive clustering -- , Practical issues -- , Incomplete data -- , Noisy data -- , Conclusion -- , Further readings -- , References -- , Basic statistics -- , Introduction -- , Notational conventions -- , Basic statistics as modeling -- , Distribution description -- , Continuous attributes -- , Discrete attributes -- , Confidence intervals -- , m-Estimation -- , Relationship detection -- , Significance tests -- , Continuous attributes -- , Discrete attributes -- , Mixed attributes -- , Relationship detection caveats -- , Visualization -- , Boxplot -- , Histogram -- , Barplot -- , Conclusion -- , Further readings -- , References -- , Classification -- , Decision trees -- , Introduction -- , Decision tree model -- , Nodes and branches -- , Leaves -- , Split types -- , Growing -- , Algorithm outline -- , Class distribution calculation -- , Class label assignment -- , Stop criteria -- , Split selection -- , Split application -- , Complete process -- , Pruning -- , Pruning operators -- , Pruning criterion -- , Pruning control strategy -- , Conversion to rule sets -- , Prediction -- , Class label prediction -- , Class probability prediction -- , Weighted instances -- , Missing value handling -- , Fractional instances -- , Surrogate splits -- , Conclusion -- , Further readings -- , References -- , Naive Bayes classifier -- , Introduction -- , Bayes rule -- , Classification by Bayesian inference -- , Conditional class probability -- , Prior class probability -- , Independence assumption -- , Conditional attribute value probabilities -- , Model construction -- , Prediction -- , Practical issues -- , Zero and small probabilities -- , Linear classification -- , Continuous attributes -- , Missing attribute values -- , Reducing naivety -- , Conclusion -- , Further readings -- , References -- , Linear classification -- , Introduction -- , Linear representation -- , Inner representation function -- , Outer representation function -- , Threshold representation -- , Logit representation -- , Parameter estimation -- , Delta rule -- , Gradient descent -- , Distance to decision boundary -- , Least squares -- , Discrete attributes -- , Conclusion -- , Further readings -- , References -- , Misclassification costs -- , Introduction -- , Cost representation -- , Cost matrix -- , Per-class cost vector -- , Instance-specific costs -- , Incorporating misclassification costs -- , Instance weighting -- , Instance resampling -- , Minimum-cost rule -- , Instance relabeling -- , Effects of cost incorporation -- , Experimental procedure -- , Conclusion -- , Further readings -- , References -- , Classification model evaluation -- , Introduction -- , Dataset performance -- , Training performance -- , True performance -- , Performance measures -- , Misclassification error -- , Weighted misclassification error -- , Mean misclassification cost -- , Confusion matrix -- , ROC analysis -- , Probabilistic performance measures -- , Evaluation procedures -- , Model evaluation vs. modeling procedure evaluation. , Evaluation caveats -- , Hold-out -- , Cross-validation -- , Leave-one-out -- , Bootstrapping -- , Choosing the right procedure -- , Evaluation procedures for temporal data -- , Conclusion -- , Further readings -- , References -- , Regression -- , Linear regression -- , Introduction -- , Linear representation -- , Parametric representation -- , Linear representation function -- , Nonlinear representation functions -- , Parameter estimation -- , Mean square error minimization -- , Delta rule -- , Gradient descent -- , Least squares -- , Discrete attributes -- , Advantages of linear models -- , Beyond linearity -- , Generalized linear representation -- , Enhanced representation -- , Polynomial regression -- , Piecewise-linear regression -- , Conclusion -- , Further readings -- , References -- , Regression trees -- , Introduction -- , Regression tree model -- , Nodes and branches -- , Leaves -- , Split types -- , Piecewise-constant regression -- , Growing -- , Algorithm outline -- , Target function summary statistics -- , Target value assignment -- , Stop criteria -- , Split selection -- , Split application -- , Complete process -- , Pruning -- , Pruning operators -- , Pruning criterion -- , Pruning control strategy -- , Prediction -- , Weighted instances -- , Missing value handling -- , Fractional instances -- , Surrogate splits -- , Piecewise linear regression -- , Growing -- , Pruning -- , Prediction -- , Conclusion -- , Further readings -- , References -- , Regression model evaluation -- , Introduction -- , Dataset performance -- , Training performance -- , True performance -- , Performance measures -- , Residuals -- , Mean absolute error -- , Mean square error -- , Root mean square error -- , Relative absolute error -- , Coefficient of determination -- , Correlation -- , Weighted performance measures -- , Loss functions -- , Evaluation procedures -- , Hold-out -- , Cross-validation -- , Leave-one-out -- , Bootstrapping -- , Choosing the right procedure -- , Conclusion -- , Further readings -- , References -- , Clustering -- , (Dis)similarity measures -- , Introduction -- , Measuring dissimilarity and similarity -- , Difference-based dissimilarity -- , Euclidean distance -- , Minkowski distance -- , Manhattan distance -- , Canberra distance -- , Chebyshev distance -- , Hamming distance -- , Gower's coefficient -- , Attribute weighting -- , Attribute transformation -- , Correlation-based similarity -- , Discrete attributes -- , Pearson's correlation similarity -- , Spearman's correlation similarity -- , Cosine similarity -- , Missing attribute values -- , Conclusion -- , Further readings -- , References -- , K-Centers clustering -- , Introduction -- , Basic principle -- , (Dis)similarity measures -- , Algorithm scheme -- , Initialization -- , Stop criteria -- , Cluster formation -- , Implicit cluster modeling -- , Instantiations -- , k-Means -- , Center adjustment -- , Minimizing dissimilarity to centers -- , Beyond means -- , k-Medians -- , k-Medoids -- , Beyond (fixed) k -- , Multiple runs -- , Adaptive k-centers -- , Explicit cluster modeling -- , Conclusion -- , Further readings -- , References -- , Hierarchical clustering -- , Introduction -- , Basic approaches -- , (Dis)similarity measures -- , Cluster hierarchies -- , Motivation -- , Model representation -- , Agglomerative clustering -- , Algorithm scheme -- , Cluster linkage -- , Divisive clustering -- , Algorithm scheme -- , Wrapping a flat clustering algorithm -- , Stop criteria -- , Hierarchical clustering visualization -- , Hierarchical clustering prediction -- , Cutting cluster hierarchies -- , Cluster membership assignment -- , Conclusion -- , Further readings -- , References -- , Clustering model evaluation -- , Introduction -- , Dataset performance. , Training performance -- , True performance -- , Per-cluster quality measures -- , Diameter -- , Separation -- , Isolation -- , Silhouette width -- , Davies -- Bouldin index -- , Overall quality measures -- , Dunn index -- , Average Davies-Bouldin index -- , C index -- , Average silhouette width -- , Loglikelihood -- , External quality measures -- , Misclassification error -- , Rand index -- , General relationship detection measures -- , Using quality measures -- , Conclusion -- , Further readings -- , References -- , Getting Better Models -- , Model ensembles -- , Introduction -- , Model committees -- , Base models -- , Different training sets -- , Different algorithms -- , Different parameter setups -- , Algorithm randomization -- , Base model diversity -- , Model aggregation -- , Voting/Averaging -- , Probability averaging -- , Weighted voting/averaging -- , Using as attributes -- , Specific ensemble modeling algorithms -- , Bagging -- , Stacking -- , Boosting -- , Random forest -- , Random Naive Bayes -- , Quality of ensemble predictions -- , Conclusion -- , Further readings -- , References -- , Kernel methods -- , Introduction -- , Support vector machines -- , Classification margin -- , Maximum-margin hyperplane -- , Primal form -- , Dual form -- , Soft margin -- , Support vector regression -- , Regression tube -- , Primal form -- , Dual form -- , Kernel trick -- , Kernel functions -- , Linear kernel -- , Polynomial kernel -- , Radial kernel -- , Sigmoid kernel -- , Kernel prediction -- , Kernel-based algorithms -- , Kernel-based SVM -- , Kernel-based SVR -- , Conclusion -- , Further readings -- , References -- , Attribute transformation -- , Introduction -- , Attribute transformation task -- , Target task -- , Target attribute -- , Transformed attribute -- , Training set -- , Modeling transformations -- , Nonmodeling transformations -- , Simple transformations -- , Standardization -- , Normalization -- , Aggregation -- , Imputation -- , Binary encoding -- , Multiclass encoding -- , Encoding and decoding functions -- , 1-ok-k encoding -- , Error-correcting encoding -- , Effects of multiclass encoding -- , Conclusion -- , Further readings -- , References -- , Discretization -- , Introduction -- , Discretization task -- , Motivation -- , Task definition -- , Discretization as modeling -- , Discretization quality -- , Unsupervised discretization -- , Equal-width intervals -- , Equal-frequency intervals -- , Nonmodeling discretization -- , Supervised discretization -- , Pure-class discretization -- , Bottom-up discretization -- , Top-down discretization -- , Effects of discretization -- , Conclusion -- , Further readings -- , References -- , Attribute selection -- , Introduction -- , Attribute selection task -- , Motivation -- , Task definition -- , Algorithms -- , Attribute subset search -- , Search task -- , Initial state -- , Search operators -- , State selection -- , Stop criteria -- , Attribute selection filters -- , Simple statistical niters -- , Correlation-based filters -- , Consistency-based filters -- , Relief -- , Random forest -- , Cutoff criteria -- , Filter-driven search -- , Attribute selection wrappers -- , Subset evaluation -- , Wrapper attribute selection -- , Effects of attribute selection -- , Conclusion -- , Further readings -- , References -- , Case studies -- , Introduction -- , Datasets -- , Packages -- , Auxiliary functions -- , Census income -- , Data loading and preprocessing -- , Default model -- , Incorporating misclassification costs -- , Pruning -- , Attribute selection -- , Final models -- , Communities and crime -- , Data loading -- , Data quality -- , Regression trees -- , Linear models -- , Attribute selection -- , Piecewise-linear models -- , Cover type -- , Data loading and preprocessing -- , Class imbalance -- , Decision trees -- , Class rebalancing -- , Multiclass encoding -- , Final classification models -- , Clustering -- , Conclusion -- , Further readings -- , References -- , Closing -- , Notation -- , Attribute values -- , Data subsets -- , Probabilities -- , R packages -- , CRAN packages -- , DMR packages -- , Installing packages -- , References -- , Datasets.

Weitere Ausg.: Print version: Cichosz, Pawel. Data mining algorithms. Chichester, West Sussex, United Kingdom : Wiley, 2015 ISBN 9781118332580

Sprache: Englisch

Schlagwort(e): Electronic books.

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118950951

UID:

Umfang: 1 Online-Ressource (XXXI, 683 Seiten) : , Illustrationen, Diagramme.

ISBN: 978-1-118-95095-1

Weitere Ausg.: Erscheint auch als Druck-Ausgabe ISBN 978-1-118-33258-0

Sprache: Englisch

Fachgebiete: Informatik , Wirtschaftswissenschaften

Schlagwort(e): Data Mining ; R

DOI: 10.1002/9781118950951

URL: Volltext  (URL des Erstveröffentlichers)

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Umfang: XXXI, 683 S. , Ill., graph. Darst. , 252 mm

ISBN: 9781118332580 , 9781118950807

Inhalt: "This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

Inhalt: "This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--

Anmerkung: Author: Paweł Cichosz (Department of Electronics and Information Technology, Warsaw University of Technology, Poland) , Includes bibliographical references and index

Weitere Ausg.: ISBN 9781118950845

Weitere Ausg.: ISBN 9781118950807

Weitere Ausg.: Erscheint auch als Online-Ausgabe Cichosz, Paweł Data mining algorithms Chichester, West Sussex : John Wiley & Sons Inc, 2015 ISBN 1118950844

Weitere Ausg.: ISBN 1118950801

Weitere Ausg.: ISBN 9781118950807

Weitere Ausg.: ISBN 9781118950845

Weitere Ausg.: ISBN 1322317461

Weitere Ausg.: ISBN 9781322317465

Weitere Ausg.: ISBN 9781118950951

Weitere Ausg.: ISBN 111895095X

Weitere Ausg.: Online-Ausg. Cichosz, Paweł Data mining algorithms Malden, MA : Blackwell Publishing, 2015 ISBN 9781118950951

Sprache: Englisch

Fachgebiete: Informatik , Wirtschaftswissenschaften

Schlagwort(e): Data Mining ; R

URL: Inhaltsverzeichnis

URL: Cover

URL: Inhaltstext