Paper Title:
A New Information Entropy-Based Ant Clustering Algorithm
  Abstract

Ant-based clustering is a heuristic clustering method that draws its 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 a New Information Entropy-based Ant Clustering (NIEAC) algorithm. Firstly, we apply 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.

  Info
Periodical
Edited by
Ford Lumban Gaol, Mehdi Roopaei, Svetlana Perry and Jessica Xu
Pages
101-105
DOI
10.4028/www.scientific.net/AMM.87.101
Citation
W. L. Zhao, Z. G. Zhang, Z. J. Zhang, "A New Information Entropy-Based Ant Clustering Algorithm", Applied Mechanics and Materials, Vol. 87, pp. 101-105, 2011
Online since
August 2011
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