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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 noncarrier 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.

Availability of large full-text document collection in electronic forms has created a need for tools techniques that assist users in organization. Document clustering is one of the popular methods used for this purpose. Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm. The ant behavior model is modified to pursue better algorithmic performance. In this paper, a hybrid approach based on Ant clustering and Fuzzy clustering methods is used. First ant based clustering is used for creating raw and imprecise clusters and then these clusters are refined by means of fuzzy C-Mean (FCM) algorithm. For large datasets these two stages does not suffice and many homogenous small clusters are formed. Thus more iteration of these two stages is usually required and clusters from previous iterations are used as a building block in the following iterations to build finer and larger clusters. The proposed algorithm is tested with a sample set of documents excerpted from the Reuters-21578 corpus and the experiment results partly indicate that the proposed algorithm perform better than the standard ant-based text-clustering algorithm and the k-means algorithm.

In this paper, we propose a new method for data mining based on Ant Colony Optimization (ACO).The ACO is a metheuristic inspired by the behavior of real ants, where they search for optimal solutions by considering both local heuristic and previous knowledge, observed by pheromone changes. Good results of the ant colony algorithms for solving combinatorial optimization problems suggest an appropriate effectiveness of the approach also in the task of discovering classification rules in data mining. Mining classification rules is an important research area in data mining. Ant-Miner is an Ant Colony Optimization algorithm for classification task. This paper proposes an improved version of Ant-Miner named Ant-Miner4, which is based on Ant-Miner3 By changing the heuristic function used in the Ant-Miner3, and implementing it based on correction function of Laplace, we tried to redesign Ant-Miner to gain rules with high predictive accuracy. We compared Ant-Miner4 with the previous version (Ant-Miner3) using four data sets. The results indicated that the accuracy of the rules discovered by the new version was higher than the ones gained by the previous version.

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