Mathematics Letters

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Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm

Received: Aug. 23, 2019    Accepted: Sep. 06, 2019    Published: Sep. 23, 2019
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Abstract

A genetic algorithm for mixture model clustering using variable data segmentation and model selection is proposed in this study. Principle of the method is demonstrated on mixture model clustering of Ruspini data set. The segment numbers of the variables in the data set were determined and the variables were converted into categorical variables. It is shown that variable data segmentation forms the number and structure of cluster centers in data. Genetic Algorithms were used to determine the number of finite mixture models. The number of total mixture models and possible candidate mixture models among them are calculated using cluster centers formed by variable data segmentation in data set. Mixture of normal distributions is used in mixture model clustering. Maximum likelihood, AIC and BIC values were obtained by using the parameters in the data for each candidate mixture model. Candidate mixture models are established, to determine the number and structure of clusters, using sample means and variance-covariance matrices for data set. The best mixture model for model based clustering of data is selected according to information criteria among possible candidate mixture models. The number of components in the best mixture model corresponds to the number of clusters, and the components of the best mixture model correspond to the structure of clusters in data set.

DOI 10.11648/j.ml.20190502.12
Published in Mathematics Letters ( Volume 5, Issue 2, June 2019 )
Page(s) 23-32
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Cluster Centers, Data Clustering, Data Mining, Genetic Algorithm, Information Criteria, Mixture Model Clustering, Model Selection, Variable Data Segmentation

References
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[6] Soffritti, G. (2003). Identifying multiple cluster structures in a data matrix. Communications in Statistics, Simulation & Computation, Vol. 32, Issue 4, pp. 1151-1181.
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    Maruf Gogebakan, Hamza Erol. (2019). Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm. Mathematics Letters, 5(2), 23-32. https://doi.org/10.11648/j.ml.20190502.12

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    ACS Style

    Maruf Gogebakan; Hamza Erol. Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm. Math. Lett. 2019, 5(2), 23-32. doi: 10.11648/j.ml.20190502.12

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    AMA Style

    Maruf Gogebakan, Hamza Erol. Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm. Math Lett. 2019;5(2):23-32. doi: 10.11648/j.ml.20190502.12

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  • @article{10.11648/j.ml.20190502.12,
      author = {Maruf Gogebakan and Hamza Erol},
      title = {Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm},
      journal = {Mathematics Letters},
      volume = {5},
      number = {2},
      pages = {23-32},
      doi = {10.11648/j.ml.20190502.12},
      url = {https://doi.org/10.11648/j.ml.20190502.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ml.20190502.12},
      abstract = {A genetic algorithm for mixture model clustering using variable data segmentation and model selection is proposed in this study. Principle of the method is demonstrated on mixture model clustering of Ruspini data set. The segment numbers of the variables in the data set were determined and the variables were converted into categorical variables. It is shown that variable data segmentation forms the number and structure of cluster centers in data. Genetic Algorithms were used to determine the number of finite mixture models. The number of total mixture models and possible candidate mixture models among them are calculated using cluster centers formed by variable data segmentation in data set. Mixture of normal distributions is used in mixture model clustering. Maximum likelihood, AIC and BIC values were obtained by using the parameters in the data for each candidate mixture model. Candidate mixture models are established, to determine the number and structure of clusters, using sample means and variance-covariance matrices for data set. The best mixture model for model based clustering of data is selected according to information criteria among possible candidate mixture models. The number of components in the best mixture model corresponds to the number of clusters, and the components of the best mixture model correspond to the structure of clusters in data set.},
     year = {2019}
    }
    

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    T1  - Mixture Model Clustering Using Variable Data Segmentation and Model Selection: A Case Study of Genetic Algorithm
    AU  - Maruf Gogebakan
    AU  - Hamza Erol
    Y1  - 2019/09/23
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    DO  - 10.11648/j.ml.20190502.12
    T2  - Mathematics Letters
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    JO  - Mathematics Letters
    SP  - 23
    EP  - 32
    PB  - Science Publishing Group
    SN  - 2575-5056
    UR  - https://doi.org/10.11648/j.ml.20190502.12
    AB  - A genetic algorithm for mixture model clustering using variable data segmentation and model selection is proposed in this study. Principle of the method is demonstrated on mixture model clustering of Ruspini data set. The segment numbers of the variables in the data set were determined and the variables were converted into categorical variables. It is shown that variable data segmentation forms the number and structure of cluster centers in data. Genetic Algorithms were used to determine the number of finite mixture models. The number of total mixture models and possible candidate mixture models among them are calculated using cluster centers formed by variable data segmentation in data set. Mixture of normal distributions is used in mixture model clustering. Maximum likelihood, AIC and BIC values were obtained by using the parameters in the data for each candidate mixture model. Candidate mixture models are established, to determine the number and structure of clusters, using sample means and variance-covariance matrices for data set. The best mixture model for model based clustering of data is selected according to information criteria among possible candidate mixture models. The number of components in the best mixture model corresponds to the number of clusters, and the components of the best mixture model correspond to the structure of clusters in data set.
    VL  - 5
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Author Information
  • Department of Maritime Business and Administration, Maritime Faculty, Bandirma Onyedi Eylul University, Bandirma, Turkey

  • Department of Computer Engineering, Faculty of Engineering, Mersin University, Mersin, Turkey

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