Word Categorization Using Clustering Ensemble

Entropy model is the base structure of automated word categorizing. In this model, words appear consecutive frequently will place in different groups. Although this method is not correct always, in the most cases, obtained results simulate real situation. Because of NP-complete structure of clustering problems, the entropy model cannot be solved by an optimal algorithm, so a number of heuristic algorithms were developed to solve this problem. Some examples of these heuristics are artificial neural networks, genetic algorithms, greedy algorithms and so on. In this paper, we used a clustering ensemble method for word categorization. The method uses a new feature space generated by k-means for clustering. The results show that an improvement in categorization is obtained.