An Improved Ant Clustering Algorithm Based on Habitation-Searching
An Improved Ant Clustering Algorithm based on habitation-searching is proposed to solve the clustering problem in data mining. In this algorithm, each ant stands for one data object, and the ants search suitable places to stay according to the probability function for ants becoming active and the clustering rules, which are given in the paper. The ants affect each other in the process, in this way the clustering will be formed by dynamic self-organization for the ants. Besides, in order to improve the quality and speed of the clustering, the activation threshold changes adaptively as the algorithm runs. The achieved results are compared with those obtained by LF algorithm, showing that significant improvements are obtained by the proposed method, and demonstrating the effectiveness of the algorithm.
KeywordsAnt clustering algorithm Habitation model Probability activation function Data mining
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