Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Support Vector Machines for Text Categorization
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 4 - Volume 4
Massively Parallel Distributed Feature Extraction in Textual Data Mining Using HDDI(tm)
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Data And Text Mining: A Business Application Approach
Data And Text Mining: A Business Application Approach
A New Association Rule-Based Text Classifier Algorithm
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
A Novel Text Classification Algorithm Based on Naïve Bayes and KL-Divergence
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
A Novel Multilingual Text Categorization System using Latent Semantic Indexing
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
An Adaptive Fuzzy kNN Text Classifier Based on Gini Index Weight
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
A comparison of feature selection methods for an evolving RSS feed corpus
Information Processing and Management: an International Journal - Special issue: Informetrics
WAIMW '06 Proceedings of the Seventh International Conference on Web-Age Information Management Workshops
A sequential niche technique for multimodal function optimization
Evolutionary Computation
Genetic algorithm for text clustering based on latent semantic indexing
Computers & Mathematics with Applications
Aircraft interior failure pattern recognition utilizing text mining and neural networks
Journal of Intelligent Information Systems
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Nowadays a vast amount of textual information is collected and stored in various databases around the world, including the Internet as the largest database of all. This rapidly increasing growth of published text means that even the most avid reader cannot hope to keep up with all the reading in a field and consequently the nuggets of insight or new knowledge are at risk of languishing undiscovered in the literature. Text mining offers a solution to this problem by replacing or supplementing the human reader with automatic systems undeterred by the text explosion. It involves analyzing a large collection of documents to discover previously unknown information. Text clustering is one of the most important areas in text mining, which includes text preprocessing, dimension reduction by selecting some terms (features) and finally clustering using selected terms. Feature selection appears to be the most important step in the process. Conventional unsupervised feature selection methods define a measure of the discriminating power of terms to select proper terms from corpus. However up to now the valuation of terms in groups has not been investigated in reported works. In this paper a new and robust unsupervised feature selection approach is proposed that evaluates terms in groups. In addition a new Modified Term Variance measuring method is proposed for evaluating groups of terms. Furthermore a genetic based algorithm is designed and implemented for finding the most valuable groups of terms based on the new measure. These terms then will be utilized to generate the final feature vector for the clustering process . In order to evaluate and justify our approach the proposed method and also a conventional term variance method are implemented and tested using corpus collection Reuters-21578. For a more accurate comparison, methods have been tested on three corpuses and for each corpus clustering task has been done ten times and results are averaged. Results of comparing these two methods are very promising and show that our method produces better average accuracy and F1-measure than the conventional term variance method.