Feature Selection for Clustering Problems: a Hybrid Algorithm that Iterates Between k-means and a Bayesian Filter.
Eduardo R. HruschkaThiago F. CovoesEstevam R. Hruschka Jr.Nelson F. F. EbeckenPublished in: HIS (2005)
Keyphrases
- hybrid algorithm
- k means
- standard test problems
- feature selection
- clustering algorithm
- high dimensionality
- clustering method
- imperialist competitive algorithm
- hierarchical clustering
- self organizing maps
- tabu search
- hybrid optimization algorithm
- spectral clustering
- data clustering
- particle swarm optimization pso
- particle swarm optimization
- initial solution
- artificial bee colony algorithm
- data mining tasks
- document clustering
- simulated annealing
- cluster analysis
- unsupervised learning
- optimization problems
- constrained clustering
- vehicle routing problems with time windows
- special case
- genetic programming
- fuzzy k means
- ant algorithm
- hybrid particle swarm optimization
- genetic algorithm
- cluster ensemble
- clustering approaches
- clustering quality
- neuro fuzzy
- benchmark problems
- support vector machine
- mutual information
- combinatorial optimization
- differential evolution