A Novel Document Clustering Algorithm Based on Ant Colony Optimization Algorithm

Document clustering based on ant colony optimization algorithm has lately attracted the attention of many scholars throughout the globe. The aim of document clustering is to place similar content in one group, and non-similar contents in separate groups. In this article, by changing the behavior model of ant movement, we attempt to upgrade the standard ant’s clustering algorithm. Ants’ movement is completely random in the standard clustering algorithm. On the one hand, we improve the algorithm’s efficiency by making ant movements purposeful, and on the other hand, by changing the rules of ant movement, we provide conditions so that the carrier ant moves to a location with intensive similarity with the carried component, and the non-carrier ant moves to a location where a component is surrounded by dissimilar components. We tested our proposed algorithm on a set of documents extracted from the 21578 Reuters Information Bank. Results show that the proposed algorithm on presents a better average performance compared to the standard ants clustering algorithm, and the K-means algorithm.