The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Improved Ant-Based Clustering and Sorting
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An artificial immune system approach to document clustering
Proceedings of the 2005 ACM symposium on Applied computing
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning and Intelligent Optimization
Exploring ant-based algorithms for gene expression data analysis
Artificial Intelligence in Medicine
Ant-MST: an ant-based minimum spanning tree for gene expression data clustering
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
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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.