HDACC: A Heuristic Density-Based Ant Colony Clustering Algorithm

  • Authors:

  • Yun-Fei Chen;Chanadio Abdul Fattah;Yu-Shu Liu;Gangway Yan

  • Affiliations:

  • Beijing Institute of Technology, Beijing, China;Engineering University Technology, Nawobshah Sindh, Pakistein;Beijing Institute of Technology, Beijing, China;Beijing Institute of Technology, Beijing, China

  • Venue:
  • IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
  • Year:
  • 2004

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present in this paper a new Heuristic Density-based Ant Colony Clustering Algorithm (HDACC). Firstly, the device of 'memory bank' is proposed, which can bring forth heuristic knowledge guiding ant to move in the bi-dimensional grid space. Hence the randomness of the ant's motion decreases and the algorithm's convergence speeds up. In addition, the memory bank makes it possible for every object to be inspected before the algorithm is terminated, which avoids the production of "un-assigned data object". So the classification error rate drops subsequently. Secondly, we proposed a density-based method which permits each ant to "look ahead", which reduces the times of region-inquiry. Consequently the clustering time gets saved. We made some experiments on real data sets and synthetic data sets. The results demonstrated that HDBCSI is a viable and effective clustering algorithm.