Determining the number of clusters in a data set is a significant and difficult problem in cluster analysis. In this study, a new model-based clustering approach is proposed for the estimation of the number of clusters. In the proposed method, the number of components in each variable is determined by using univariate Gaussian mixture models. The number of alternative cluster centres and mixture models was determined according to the number of components in heterogeneous variables. In this study, appropriate Gaussian mixture models were determined with the help of "mixture model soft computing method" for the first time. Vector arrays showing the number and addresses of clusters in appropriate Gaussian mixture models were created, and according to the parameter estimations of these models that fit the arrays, the best model was obtained through information criteria. The clustering success achieved with the proposed mixture model soft computing method was compared with the results of Gaussian mixture model clustering methods namely, mclust, clustvarsel, varselLCM, selvarMix and vscc model selection methods in R package. All respective methods analyse and determine the number of clustering for the data sets, synthetic-1, synthetic-2, Iris, and Landsat Satellite Image data sets, respectively and evaluate the correct classification rate. The results revealed that the proposed method shows better results for the determination of number of clustering as well as correct classification rate. The novelty of the study is that a new model-based dimension reduction method is proposed for the estimation of the number of clusters. A deterministic clustering approach is proposed for clustering and classification success on reduced data.

Spontaneous combustion of coal is a complex underground mining disaster, which mainly threats mine safety and efficiency. Several factors usually cause spontaneous combustion of coal such as gas concentration, ventilation, coal properties. In this study, spontaneous combustion tendencies of the coal mines were predicted considering the effective parameters for an underground coal mine in Turkey. The Multinomial Logistic Regression which is a multivariate statistical technique was applied. Gas concentrations (CH₄, CO, O₂) and air velocity were defined as factors affecting spontaneous coal combustion. Fire hazard levels of the coal mines were determined as “normal situation” and “potential combustion”. It was observed that CH₄, CO variables, and CH₄*CO interaction were effective in the formation of clusters. The results indicate that Mine I is more liable to spontaneous combustion than Mine II and Mine III. At the same time, the effects of variations in factors are examined in the study.

Günümüz dünyasında ülkeler arasındaki ticaretin artması ve çeşitlenmesi lojistik faaliyetlerini oldukça önemli bir yere taşımaktadır. Ülkeler rekabet gücü ve ekonomik üstünlük elde etmek için lojistik performanslarını etkileyen unsurları belirleyerek performanslarını arttırmaya çalışmaktadır. Lojistik performans değerlendirmelerine göre bulundukları sıralamalar uluslararası ticarette ülkelere oldukça önemli avantajlar sağlamaktadır. Bu nedenle, Dünya Bankası iki yılda bir 160 ülkenin Lojistik Performans Endeksini (LPI) hesaplayıp ülkelerin sıralamalarını raporlamaktadır. Bu çalışmada, lojistik performansı etkileyen göstergelerin önem derecesine göre yeniden sıralanması için Entropi tabanlı TOPSIS yöntemine dayalı yeni bir sıralama ve değişken seçim yaklaşımı önerilmiştir. Önerilen yöntemde değişkenlerin ağırlıklandırılması Entropi yöntemine dayılı belirlenip TOPSIS yöntemi ile sıralama skorlarının belirlenmesi için algoritma kullanılmıştır. Ağırlıklandırılmış değişkenlere göre sıralama yöntemi sonuçlarına dayanarak ülkelerin sıralamalarındaki değişiklikler belirlenmiş ve nedenleri tespit edilmiştir. Değişken seçimi ile ağırlıklandırma ile yapılan sıralamada 65 ülkenin sıralaması yükselirken 76 ülkenin sıralaması düşmüş ve 19 ülkenin sırasında bir değişiklik gözlenmemiştir. Ağırlıklandırma yöntemine göre ülkelerin lojistik performansı üzerindeki en etkili değişken %23 ile altyapı göstergesi olmuştur.

Cervical cancer is one of the most successful types of treatment when diagnosed early. In this study, it is aimed to find and classify the disease with data mining methods on the digitized data set obtained as a result of the pap-smear test. Two-stage architecture has been proposed for the diagnosis of cervical cancer. In the first stage of the study, missing data were extracted from the used dataset, and in the second stage, a new dataset was obtained by using the Synthetic Minority Oversampling Technique (SMOTE) algorithm to balance the target classes in the dataset. By applying the majority voting (MV) method to the dataset used in the study, the structure with 4 target variables was reduced to a single target variable. On two data sets, Artificial Neural Network (ANN), Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms from data mining methods were used for the diagnosis of cervical cancer. The results obtained from the original dataset and the dataset produced with Smote were compared. ANN is the best method evaluated according to classification success and F-score, and the major voted target variable in the balanced data group produced with the Smote algorithm gave the most successful result. The experimental results showed that the use of MV and SMOTE algorithms together increased the classification success from 93% to 99%.