WordNet: a lexical database for English
Communications of the ACM
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
A novel word clustering algorithm based on latent semantic analysis
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
A Graph-Theoretic Approach to Nonparametric Cluster Analysis
IEEE Transactions on Computers
A Branch and Bound Clustering Algorithm
IEEE Transactions on Computers
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithm-based text clustering technique
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Document similarity: a new measure using OWA
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Time complexity estimation and optimisation of the genetic algorithm clustering method
WSEAS Transactions on Mathematics
An improved genetic algorithm for optimal feature subset selection from multi-character feature set
Expert Systems with Applications: An International Journal
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A multi-layer text classification framework based on two-level representation model
Expert Systems with Applications: An International Journal
Ontology-based semantic similarity: A new feature-based approach
Expert Systems with Applications: An International Journal
Efficient stochastic algorithms for document clustering
Information Sciences: an International Journal
Summarising customer online reviews using a new text mining approach
International Journal of Business Information Systems
Hi-index | 12.06 |
This paper proposes a self-organized genetic algorithm for text clustering based on ontology method. The common problem in the fields of text clustering is that the document is represented as a bag of words, while the conceptual similarity is ignored. We take advantage of thesaurus-based and corpus-based ontology to overcome this problem. However, the traditional corpus-based method is rather difficult to tackle. A transformed latent semantic indexing (LSI) model which can appropriately capture the associated semantic similarity is proposed and demonstrated as corpus-based ontology in this article. To investigate how ontology methods could be used effectively in text clustering, two hybrid strategies using various similarity measures are implemented. Experiments results show that our method of genetic algorithm in conjunction with the ontology strategy, the combination of the transformed LSI-based measure with the thesaurus-based measure, apparently outperforms that with traditional similarity measures. Our clustering algorithm also efficiently enhances the performance in comparison with standard GA and k-means in the same similarity environments.