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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 اصل مقاله (743.45 K) | ||
| 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 | ||
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آمار تعداد مشاهده مقاله: 171 تعداد دریافت فایل اصل مقاله: 53 |
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