Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering.
Laith Mohammad AbualigahAhamad Tajudin KhaderPublished in: J. Supercomput. (2017)
Keyphrases
- text clustering
- particle swarm optimization algorithm
- genetic operators
- evolution strategy
- text categorization
- feature selection
- evolutionary algorithm
- genetic algorithm
- text mining
- particle swarm optimization
- convergence speed
- fitness function
- differential evolution
- mutation operator
- document clustering
- text data
- text documents
- crossover and mutation
- clustering algorithm
- text classification
- particle swarm
- text collections
- pso algorithm
- unsupervised learning
- hierarchical clustering
- background knowledge
- global search
- genetic algorithm ga
- k means
- supervised learning
- multi objective
- crossover operator
- genetic programming
- wordnet
- semi supervised
- self organizing maps
- probabilistic model
- premature convergence
- search algorithm
- metaheuristic
- document representation
- metric learning
- knn
- artificial neural networks
- feature space
- dimensionality reduction
- language model
- particle swarm optimization pso
- machine learning
- neural network