An Improved Entropy-based Ant Clustering Algorithm
Sorting and clustering methods inspired by the behavior of real ants are among the earliest methods in ant-based meta-heuristics. We revisit these methods in the context of a concrete application and introduce some modifications that yield significant improvements in terms of both quality and efficiency. In this paper, we propose an Improved Entropy-based Ant Clustering (IEAC) algorithm. Firstly, we apply information entropy to model behaviors of agents, such as picking up and dropping objects. The entropy function led to better quality clusters than non-entropy functions. Secondly, we introduce a number of modifications that improve the quality of the clustering solutions generated by the algorithm. We have made some experiments on real data sets and synthetic data sets. The results demonstrate that our algorithm has superiority in misclassification error rate and runtime over the classical algorithm.
cluster analysis ant-based algorithm information entropy
Zhao Weili
College of Sciences Shenyang Ligong University Shenyang, China
国际会议
2009 WASE International Conference on Information Engineering(2009年国际信息工程会议)(ICIE 2009)
太原
英文
700-703
2009-07-10（万方平台首次上网日期，不代表论文的发表时间）