使用真实蚂蚁行为的数据流增量聚类

N. Masmoudi, Hanene Azzag, M. Lebbah, C. Bertelle
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引用次数: 2

摘要

本文提出了一种新的用于数据增量聚类的仿生方法CL-AntInc。这个算法使用了真实蚂蚁的行为。我们通过聚类启发式来处理数据量问题。根据群体气味和信息素机制的模拟,构建了动态图。我们使用了从机器学习存储库中提取的数值数据库。实验结果表明了该算法的有效性。
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Incremental clustering of data stream using real ants behavior
We present in this paper a new biomimetic method nammed CL-AntInc for data incremental clustering. This algorithm uses the behavior of real ants. We deal with the issue of data volume through a clustering heuristic. Dynamic graphs are constructed according to a simulation of colonial odors and pheromone mechanisms. We used numerical databases extracted from the Machine Learning Repository. The experimental results show the effectiveness of the suggested algorithm.
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