An Evolutionary Approach for Document Clustering

We propose an evolutionary approach based on genetic algorithm for text document clustering. Instead of applying genetic algorithm on the whole dataset, we partition the dataset into some groups and apply genetic algorithm to each of the partitions separately. Finally, we apply another genetic algorithm phase on the outcomes of the earlier ones. This allows to get rid of the local minima, which is one of the major problems of using genetic algorithms. Another good feature of our proposal is that we do not require specifying the total clusters to be made in advance as most of the available methods. Experimental results also show the superior performance of our approach as compared to the previous approaches.