Data mining algorithms : explained using R

Data mining algorithms explained using R

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

Bibliographic Details
Other Authors: Cichosz, Pawel, author (author)
Format: eBook
Language:Inglés
Published: Chichester, England : Wiley 2015.
Edition:1st ed
Subjects:
Data mining.
Computer algorithms.
R (Computer program language)
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849128506719
Table of Contents:
  • Part I. Preliminaries
  • 1. Tasks
  • 2. Basic statistics
  • Part II. Classification
  • 3. Decision trees
  • 4. Naèive Bayes classifier
  • 5. Linear classification
  • 6. Misclassification costs
  • 7. Classification model evaluation
  • Part III. Regression
  • 8. Linear regression
  • 9. Regression trees
  • 10. Regression model evaluation
  • Part IV. Clustering
  • 11. (Dis)similarity measures
  • 12. k-Centers clustering
  • 13. Hierarchical clustering
  • 14. Clustering model evaluation
  • Part V. Getting better models
  • 15. Model ensembles
  • 16. Kernel methods
  • 17. Attribute transformation
  • 18. Discretization
  • 19. Attribute selection.

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