Information Entropy-based Ant Clustering Algorithm
Ant-based clustering is a heuristic clustering method that draws
inspiration from the behavior of ants in nature. 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
Information Entropy-based Ant Clustering (IEAC) and New
Information Entropy-based Ant Clustering (NIEAC) algorithm.
Firstly, we apply information entropy and new information
entropy to model behaviors of agents, such as picking up and
dropping objects. The new 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.
Keywords: Ant Clustering, Entropy, Information Entropy, New Information Entropy
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ABOUT THE AUTHORS
Zhang Yi
Shenyang University, Shenyang, China
Zhao Weili
Shenyang Ligong University, Shenyang, China
Zhang Zhiguo
Neusoft Ltd., Shenyang, China
Zhang Yi
Shenyang University, Shenyang, China
Zhao Weili
Shenyang Ligong University, Shenyang, China
Zhang Zhiguo
Neusoft Ltd., Shenyang, China