Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article FW-S3PFCM: Feature-Weighted Safe-Semi Possibilistic Fuzzy C-Means Clustering(Springer, 2026) Khezri, Shirin; Aghazadeh, Nasser; Hashemzadeh, Mahdi; Golzari Oskouei, AminThe safe semi-supervised fuzzy c-means clustering (S3FCM) method is a well-known clustering method that can produce successful results by incorporating prior knowledge of the class distribution. Its process is fast and simple but still has two limitations. The first issue is that it gives equal weight to all data features, while in real-world applications, different features usually have different importance. Secondly, S3FCM is very sensitive to noise and outliers. This paper proposes an extension of the S3FCM, entitled FW-S3PFCM, to mitigate these shortcomings. The proposed method uses a local feature weighting scheme to consider the different feature weights in the clustering process. Additionally, a possibilistic version of the S3FCM is designed to reduce the sensitivity to noise and outliers. The effectiveness of the proposed method is comprehensively evaluated on various benchmark datasets, and its performance is compared with the state-of-the-arts methods. To practically asses the FW-S3FCM, a real-world dataset of brain MRI images and its segmentation performance are analyzed as well. The average Accuracy, F1-score, Sensitivity, and Precision measures obtained by FW-S3FCM are 0.9682, 0.9826, 0.9743, and 0.9925, respectively, which are better than the competitors' performance.Article A Robust Possibilistic Semi-Supervised Fuzzy Clustering Algorithm With Neighborhood-Aware Feature Weighting(Springer Heidelberg, 2025) Moghaddam, Arezou Najafi; Aghazadeh, Nasser; Hashemzadeh, Mahdi; Oskouei, Amin GolzariThe Semi-Supervised Fuzzy C-Means (SSFCM) method integrates class distribution information with fuzzy logic to overcome the challenges of semi-supervised clustering methods. While the inclusion of label information in the objective function improves the quality of the clustering method, semi-supervised fuzzy techniques still encounter important limitations, including (1) sensitivity to noise and outliers, (2) uniform feature importance, (3) neglecting the influences of neighborhood in the clustering process. In this paper, an improved semi-supervised clustering algorithm is presented to address these challenges. First, the algorithm reduces the sensitivity to noise and outliers by integrating the possibilistic fuzzy C-means algorithm into the SSFCM method. Second, a dynamic feature weighting method assigns different weights to the features in each cluster, which improves the performance of the algorithm in imbalanced datasets. Third, the proposed algorithm introduces a neighborhood mechanism that incorporates the neighbor's trade-off weighting and feature weighting strategy considering a strong metric. Finally, a robust kernel metric is used to further improve the performance on complex and nonlinear datasets. Extensive experiments are conducted on several benchmark datasets to evaluate the performance of the proposed method. The results show that the proposed method outperforms the current state-of-the-art techniques. The implementation source codes of the proposed method are publicly available at https://github.com/Amin-Golzari-Oskouei/RPSSFC-NAFW.