Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Genetic algorithm-based clustering ensemble: determination number of clusters

    Access Status
    Fulltext not available
    Authors
    Mohammadi, M.
    Azadeh, A.
    Saberi, Morteza
    Azaron, A.
    Date
    2010
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Mohammadi, M. and Azadeh, A. and Saberi, M. and Azaron, A. 2010. Genetic algorithm-based clustering ensemble: determination number of clusters. International Journal of Business Forecasting and Marketing Intelligence (IJBFMI). 1 (3/4): pp. 201-216.
    Source Title
    International Journal of Business Forecasting and Marketing Intelligence (IJBFMI)
    ISSN
    1744-6635
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/6819
    Collection
    • Curtin Research Publications
    Abstract

    Genetic algorithms (GAs) have been used in the clustering subject. Also, a clustering ensemble as one acceptable clustering method combines the results of multiple clustering methods on a given dataset and creates final clustering on the dataset. In this paper, genetic algorithm base on clustering ensemble (GACE) is introduced for finding optimal clusters. The most important property of our method is the ability to extract the number of clusters. With this ability, the need for data examination is removed, and then solving related problems will not be time consuming. GACE is applied to eight series of databases. Experimental results were compared with other four clustering methods. Data envelopment analysis (DEA) is used to compare methods. The results of DEA indicate that GACE is the best method. The four methods are co-association function and average link (CAL), co-association function and K-means (CK), hypergraph-partitioning algorithm (HGPA) and cluster-based similarity partitioning (CSPA).

    Related items

    Showing items related by title, author, creator and subject.

    • Techniques for improving clustering and association rules mining from very large transactional databases
      Li, Yanrong (2009)
      Clustering and association rules mining are two core data mining tasks that have been actively studied by data mining community for nearly two decades. Though many clustering and association rules mining algorithms have ...
    • An integrated artificial neural network-genetic algorithm clustering ensemble for performance assessment of decision making units
      Azadeh, A.; Saberi, Morteza; Anvari, M.; Mohamadi, M. (2011)
      This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) and genetic algorithm clustering ensemble (GACE) for measuring efficiency as a complementary tool for the ...
    • An adaptive network based fuzzy inference system–genetic algorithm clustering ensemble algorithm for performance assessment and improvement of conventional power plants
      Azadeh, A.; Saberi, Morteza; Anvari, M.; Azaron, A.; Mohammadi, M. (2011)
      Performance measurement and assessment are fundamental to management planning and control activitiesof complex systems such as conventional power plants. They have received considerable attentionby both management ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.