Feature Selection Based on the Rough Set Theory and Expectation-Maximization Clustering Algorithm.
Farideh FazayeliLipo WangJacek MandziukPublished in: RSCTC (2008)
Keyphrases
- rough set theory
- expectation maximization
- feature selection
- clustering algorithm
- rough sets
- k means
- em algorithm
- data reduction
- attribute reduction
- knowledge reduction
- decision table
- rule generation
- data analysis
- granular computing
- decision rules
- rule extraction
- probabilistic model
- knowledge discovery
- tool for data mining
- machine learning
- text categorization
- maximum likelihood
- feature selection algorithms
- information entropy
- image segmentation
- support vector
- text classification
- discernibility matrix
- feature subset
- high dimensionality
- classification accuracy
- fuzzy c means
- cluster analysis
- support vector machine
- model selection
- feature set
- equivalence relation
- fuzzy sets
- rough set model
- pattern recognition
- feature extraction
- feature space
- concept lattice
- feature ranking
- approximation spaces
- rough fuzzy
- data mining
- fuzzy clustering
- attribute set
- variable precision rough set model
- databases