Genetic guided Model based Clustering Algorithms

Hui-Dong JIN, Kwong Sak LEUNG, Man Leung WONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


Clustering or the unsupervised classification of data items into clusters can reveal some intrinsic structures among data. The intrinsic structures like the number of clusters are key issues of data mining. In this paper we propose some genetic guided model based clustering techniques to determine the optimal number of clusters and the characteristics of these clusters automatically. The model based clustering techniques are used to describe the clusters and tune their descriptions while genetic algorithms lead the search to some promising search space Several clustering-specific genetic operators are developed to enhance the search procedure The simulation results on both synthetic and real life data sets demonstrate that our proposed clustering techniques outperform two widely used model based clustering algorithms.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Artificial Intelligence : IC-A!'2001
EditorsH. R. ARABNIA
PublisherCSREA Press
ISBN (Print)1892512793
Publication statusPublished - Jun 2001
EventInternational Conference on Artificial Intelligence - Las Vegas, United States
Duration: 25 Jun 200128 Jun 2001


ConferenceInternational Conference on Artificial Intelligence
Abbreviated titleIC-AI'2001
Country/TerritoryUnited States
CityLas Vegas


  • Genetic algorithm
  • data mining
  • mixture model
  • the expectation maximization algorithm
  • number of clusters


Dive into the research topics of 'Genetic guided Model based Clustering Algorithms'. Together they form a unique fingerprint.

Cite this