A clustering entropy-driven approach for exploring and exploiting noisy functions.
Shih-Hsi LiuMarjan MernikBarrett R. BryantPublished in: SAC (2007)
Keyphrases
- information theoretic
- clustering algorithm
- information theory
- k means
- clustering method
- mutual information
- genetic algorithm
- unsupervised learning
- high dimensional data
- minimum error
- noisy data
- data clustering
- self organizing maps
- data driven
- real time
- categorical data
- dimensionality reduction
- cluster analysis
- semi supervised
- spectral clustering
- data points
- multiscale
- noisy environments
- relative entropy
- entropy measure
- neural network