Bilgisayar Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/203

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Browsing Bilgisayar Mühendisliği Bölümü Koleksiyonu by Author "0000-0001-8983-4797"

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    A Data Mining Method For Refining Groups In Data Using Dynamic Model Based Clustering
    (IEEE, 2013) Servi, Tayfun; Erol, Hamza; 0000-0001-8983-4797; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Erol, Hamza
    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.
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    A Model Selection Algorithm For Mixture Model Clustering Of Heterogeneous Multivariate Data
    (IEEE, 2013) Erol, Hamza; 0000-0001-8983-4797; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Erol, Hamza
    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.
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    Article
    A new per-field classification method using mixture discriminant analysis
    (TAYLOR & FRANCIS LTD, 2012) Calis, Nazif; Erol, Hamza; 0000-0001-8983-4797; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Erol, Hamza
    In this study, a new per-field classification method is proposed for supervised classification of remotelysensed multispectral image data of an agricultural area using Gaussian mixture discriminant analysis(MDA). For the proposed per-field classification method, multivariate Gaussian mixture models constructedfor control and test fields can have fixed or different number of components and each component can havedifferent or common covariance matrix structure. The discrimination function and the decision rule of thismethod are established according to the average Bhattacharyya distance and the minimum values of theaverage Bhattacharyya distances, respectively. The proposed per-field classification method is analyzedfor different structures of a covariance matrix with fixed and different number of components. Also, weclassify the remotely sensed multispectral image data using the per-pixel classification method based onGaussian MDA.