Data Mining Algorithms: Explained Using R

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John Wiley & Sons, Jan 27, 2015 - Computers - 716 pages

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

 

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Contents

Linear classification
134
Misclassification costs
159
Classification model evaluation
189
Linear regression
237
Regression trees
261
CONTENTS
262
Regression model evaluation
295
Dissimilarity measures
313
Attribute transformation
498
CONTENTS
512
Discretization
524
Attribute selection
558
Case studies
602
Acknowledgements xix
656
Closing
657
Datasets
666

kCenters clustering
328
CONTENTS
338
Hierarchical clustering
349
Clustering model evaluation
373
Model ensembles
403
Kernel methods
454
CONTENTS
468
Preface
xxi
Classification 69
xxiv
References
xxxi
Basic statistics
23
Decision trees
71
Na´ve Bayes classifier
118
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About the author (2015)

Pawel Cichosz, Department of Electronics and Information Technology, Warsaw University of Technology, Poland.

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