A recursive clustering methodology using a genetic algorithm

Amit Banerjee, Sushil J. Louis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

This paper presents a recursive clustering scheme that uses a genetic algorithm-based search in a dichotomous partition space. The proposed algorithm makes no assumption on the number of clusters present In the dataset; instead it recursively uncovers subsets in the data until all isolated and separated regions have been classified as clusters. A test of spatial randomness serves as a termination criteria for the recursive process. Within each recursive step, a genetic algorithm searches the partition space for an optimal dichotomy of the dataset. A simple binary representation is used for the genetic algorithm, along with classical selection, crossover and mutation operators. Results of clustering on test cases, ranging from simple datasets in 2-D to large multidimensional datasets compare favorably with state of the art approaches in genetic algorithm-driven clustering.

Original languageEnglish (US)
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages2165-2172
Number of pages8
DOIs
StatePublished - Dec 1 2007
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: Sep 25 2007Sep 28 2007

Publication series

Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

Other

Other2007 IEEE Congress on Evolutionary Computation, CEC 2007
CountrySingapore
Period9/25/079/28/07

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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    Banerjee, A., & Louis, S. J. (2007). A recursive clustering methodology using a genetic algorithm. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 2165-2172). [4424740] (2007 IEEE Congress on Evolutionary Computation, CEC 2007). https://doi.org/10.1109/CEC.2007.4424740