Browsing by Author "Erol, H."

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    A Data Mining Method for Refining Groups in Data Using Dynamic Model Based Clustering
    (IEEE, 2013) Servi, Tayfun; Erol, H.
    A new data mining method is proposed for determining the number and structure of clusters, and refining groups in multivariate heterogeneous data set including groups, partly and completely overlapped group structures by using dynamic model based clustering. It is called dynamic model based clustering since the structure of model changes at each stage of refinement process dynamically. The proposed data mining method works without data reduction for high dimensional data in which some of variables including completely overlapped situations. © 2013 IEEE. © 2013 Elsevier B.V., All rights reserved.
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    Citation - Scopus: 4
    A Model Selection Algorithm for Mixture Model Clustering of Heterogeneous Multivariate Data
    (IEEE, 2013) Erol, H.
    A model selection algorithm is developed for finding the best model among a set of mixture of normal densities fitted to heterogeneous multivariate data. Model selection algorithm proposed first finds total number of mixture of normal densities then selects possible number of mixture of normal densities and finally searches the best model among them in mixture model clustering of heterogeneous multivariate data. Log-likelihood function, Akaike's information criteria and Bayesian information criteria values are computed and graphically ploted for each mixture of normal densities. The best model is chosen according to the values of these information criterions. © 2013 IEEE. © 2013 Elsevier B.V., All rights reserved.