Browsing by Author "Erol, Hamza"

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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.
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.
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.
A New Semi-supervised Classification Method Based on Mixture Model Clustering for Classification of Multispectral Data
(SPRINGER, 233 SPRING ST, NEW YORK, NY 10013 USA, 2018) Gogebakan, Maruf; Erol, Hamza; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
A new method for semi-supervised classification of remotely-sensed multispectral image data is developed in this study. It consists of unsupervised-clustering for data labelling and supervised-classification of clusters in multispectral image data (MID) using spectral signatures. Mixture model clustering, based on model selection, is proposed for finding the number and determining the structures of clusters in MID. The best mixture model, for the best clustering of data, finds the number and determines the structure of clusters in MID. The number of elements in the best mixture model fits to the number of clusters in MID. The elements of the best mixture model fits to the structure of clusters in MID. Clusters in MID is supervised-classified using spectral signatures. Euclidean distance is used as the discrimination function for the supervised-classification method. The values of Euclidean distances are used as decision rule for the supervised-classification method.
Normal Mixture Model-Based Clustering of Data Using Genetic Algorithm
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Gogebakan, Maruf; Erol, Hamza; AGÜ; Gogebakan, Maruf
In this study, a new algorithm was developed for clustering multivariate big data. Normal mixture distributions are used to determine the partitions of variables. Normal mixture models obtained from the partitions of variables are generated using Genetic Algorithms (GA). Each partition in the variables corresponds to a clustering center in the normal mixture model. The best model that fits the data structure from normal mixture models is obtained by using the information criteria obtained from normal mixture distributions.