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37
Table of Contents
Top 10 algorithms in data mining
Abstract
Introduction
C4.5 and beyond
Introduction
Decision trees
Ruleset classifiers
See5/C5.0
Research issues
The k-means algorithm
The algorithm
Limitations
Generalizations and connections
Support vector machines
The Apriori algorithm
Description of the algorithm
The impact of the algorithm
Current and further research
The EM algorithm
Introduction
Maximum likelihood estimation of normal mixtures
Number of clusters
PageRank
Overview
The algorithm
Further references on PageRank
AdaBoost
Description of the algorithm
Impact of the algorithm
Further research
kNN: k-nearest neighbor classification
Description of the algorithm
Issues
Impact
Current and future research
Naive Bayes
Introduction
The basic principle
Some extensions
Concluding remarks on naive Bayes
CART
Overview
Splitting rules
Prior probabilities and class balancing
Missing value handling
Attribute importance
Dynamic feature construction
Cost-sensitive learning
Stopping rules, pruning, tree sequences, and tree selection
Probability trees
Theoretical foundations
Selected biographical details
Concluding remarks
Acknowledgments
References