A Novel Ant-Based Clustering Approach for Document Clustering

Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant Colony Optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper proposes a novel document clustering approach based on ACO. Unlike other ACO-based clustering approaches which are based on the same scenario that ants move around in a 2D grid and carry or drop objects to perform categorization. Our proposed ant-based clustering approach does not rely on a 2D grid structure. In addition, it can also generate optimal number of clusters without incorporating any other algorithms such as K-means or AHC. Experimental results on the subsets of 20 Newsgroup data show that the ant-based clustering approach outperforms the classical document clustering methods such as K-means and Agglomerate Hierarchical Clustering. It also achieves better results than those obtained using the Artificial Immune Network algorithm when tested in the same datasets.