Akademik Veri Yönetim Sistemi
International Journal of Computational Intelligence Systems, cilt.19, sa.1, 2026 (SCI-Expanded, Scopus)
Regression analysis estimates model parameters to describe the relationship between a dependent variable and one or more independent variables. While classical regression assumes homogeneity among observations, real-world data often exhibit heterogeneous structures. In such cases, fuzzy logic–based approaches offer an alternative for estimating the unknown parameters of regression models. However, effective results for parameter estimation cannot be obtained for ellipsoidal scattering data in these approaches either. In this study, a method based on the Gustafson-Kessel (GK) clustering algorithm has been proposed to overcome these disadvantages. Since the proposed method aims to perform parameter estimation especially on ellipsoidal data scatters, Davies-Bouldin Index (DBI), one of the clustering validity indices that provide effective results in determining the optimal number of clusters, has been adapted using the Mahalanobis distance. GK clustering algorithm was applied to obtain the membership degrees that determine the contributions of the observations in the identified clusters to the regression model to be constructed. To evaluate the performance of the proposed algorithm, applications were conducted on various datasets, and the results obtained were compared with estimates based on the Fuzzy C-Means (FCM) and Type-2 FCM clustering methods.