اخبار و اعلانات

 آمار

تعداد نشریات31
تعداد شماره‌ها836
تعداد مقالات8,060
تعداد مشاهده مقاله15,071,263
تعداد دریافت فایل اصل مقاله9,799,571
Rezaei, Marzieh, Parchami, Abbas, Sheikhi, Ayyub. (1405). A Generalization of the k-means Algorithm for Clustering Data Based on Fuzzy Population. نشریه علمی دانشگاه سیستان و بلوچستان, (), -. doi: 10.22111/ijfs.2026.53323.9441
Marzieh Rezaei; Abbas Parchami; Ayyub Sheikhi. "A Generalization of the k-means Algorithm for Clustering Data Based on Fuzzy Population". نشریه علمی دانشگاه سیستان و بلوچستان, , , 1405, -. doi: 10.22111/ijfs.2026.53323.9441
Rezaei, Marzieh, Parchami, Abbas, Sheikhi, Ayyub. (1405). 'A Generalization of the k-means Algorithm for Clustering Data Based on Fuzzy Population', نشریه علمی دانشگاه سیستان و بلوچستان, (), pp. -. doi: 10.22111/ijfs.2026.53323.9441
Rezaei, Marzieh, Parchami, Abbas, Sheikhi, Ayyub. A Generalization of the k-means Algorithm for Clustering Data Based on Fuzzy Population. نشریه علمی دانشگاه سیستان و بلوچستان, 1405; (): -. doi: 10.22111/ijfs.2026.53323.9441

A Generalization of the k-means Algorithm for Clustering Data Based on Fuzzy Population

Iranian Journal of Fuzzy Systems
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 خرداد 1405
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2026.53323.9441
نویسندگان
Marzieh Rezaei* ؛ Abbas Parchami؛ Ayyub Sheikhi
Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran
چکیده
‎Clustering is one of the data mining tools frequently used in multivariate methods to group observations into clusters based on the similarity of variables‎. ‎While many clustering algorithms have been developed for data extracted from well-defined (crisp) populations‎, ‎numerous real-world problems involve inherently fuzzy populations such as high-consumption households or high-quality products‎. ‎In such cases‎, ‎each data point is described not only by its observed values but also by its degree of membership to the fuzzy population‎. ‎This study proposes a novel clustering algorithm specifically designed for data extracted from fuzzy populations‎. ‎The proposed method generalizes the classical k-means algorithm by considering membership degrees in the clustering process‎. ‎The effectiveness of the proposed algorithm was evaluated on both synthetic and real-world datasets using several validity indices‎. ‎According to these indices‎, ‎the algorithm demonstrates satisfactory clustering quality and computational efficiency‎. The analysis of the proposed algorithm in the presence of outliers shows that, although outliers can slightly shift cluster centers, the overall clustering structure remains stable when clusters are well-separated.
کلیدواژه‌ها
k-means algorithm؛ Membership function؛ Fuzzy population؛ Weighting
آمار
تعداد مشاهده مقاله: 8


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب