Data mining algorithms : explained using R

By:

Cichosz, Pawel

Material type: Text

TextPublication details: London Wiley& Sons 2015Description: 683pISBN:

9781118332580

Subject(s):

DDC classification:

006.312 CIC/D

Contents:

Part I: Preliminaries Covers learning tasks (classification, regression, clustering), basic statistics, visualization, and practical issues. Part II: Classification Discusses decision trees, Naïve Bayes, linear classifiers, misclassification costs, and model evaluation. Part III: Regression Explores linear regression, regression trees, and performance evaluation, with extensions beyond linearity. Part IV: Clustering Focuses on similarity measures, k-means, hierarchical clustering, and quality evaluation metrics. Part V: Enhancing Models Includes ensemble methods, kernel techniques (SVMs), attribute transformation, discretization, and selection. Case Studies & Appendices Real-world applications (e.g., Census data, crime analysis), R packages, datasets, and notations.

List(s) this item appears in: New Arrivals | Dr T Jaisankar

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Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode	Item holds
Reference	IIIT Kottayam Central Library Reference	006.312 CIC/D (Browse shelf(Opens below))		Not For Loan		2304
Books	IIIT Kottayam Central Library General Stacks	006.312 CIC/D;1 (Browse shelf(Opens below))	1	Available		2305

Total holds: 0

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Previous	006.31 POW/R Reinforcement Learning and Stochastic Optimization : A Unified Framework for Sequential Decisions	006.31 PRA/A Advances in Financial Machine Learning	006.312 CHA/D Data Mining Methods	006.312 CIC/D Data mining algorithms : explained using R	006.312 DUN/D Data Mining :Introductory and Advanced Topics	006.312 HAN/D Data Mining :Concepts and Techniques	006.312 ISH/S Social Big Data Mining	Next

Includes bibliographical references and index.

Part I: Preliminaries
Covers learning tasks (classification, regression, clustering), basic statistics, visualization, and practical issues.

Part II: Classification
Discusses decision trees, Naïve Bayes, linear classifiers, misclassification costs, and model evaluation.

Part III: Regression
Explores linear regression, regression trees, and performance evaluation, with extensions beyond linearity.

Part IV: Clustering
Focuses on similarity measures, k-means, hierarchical clustering, and quality evaluation metrics.

Part V: Enhancing Models
Includes ensemble methods, kernel techniques (SVMs), attribute transformation, discretization, and selection.

Case Studies & Appendices
Real-world applications (e.g., Census data, crime analysis), R packages, datasets, and notations.

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