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An Improved Ant Clustering Algorithm Based on Habitation-Searching

  • Yu-bo Duan
  • Zhong Dai
  • Qin Chen
  • Ke-yong Shao
  • Shuang-shuang Xu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 135)

Abstract

An Improved Ant Clustering Algorithm based on habitation-searching is proposed to solve the clustering problem in data mining. In this algorithm, each ant stands for one data object, and the ants search suitable places to stay according to the probability function for ants becoming active and the clustering rules, which are given in the paper. The ants affect each other in the process, in this way the clustering will be formed by dynamic self-organization for the ants. Besides, in order to improve the quality and speed of the clustering, the activation threshold changes adaptively as the algorithm runs. The achieved results are compared with those obtained by LF algorithm, showing that significant improvements are obtained by the proposed method, and demonstrating the effectiveness of the algorithm.

Keywords

Ant clustering algorithm  Habitation model  Probability activation function  Data mining  

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Yu-bo Duan
    • 1
  • Zhong Dai
    • 2
  • Qin Chen
    • 1
  • Ke-yong Shao
    • 1
  • Shuang-shuang Xu
    • 1
  1. 1.Electrical and Information Engineering CollegeNortheast Petroleum UniversityDaqingChina
  2. 2.Natural Gas Branch of Daqing OilfieldDaqingChina

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