A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data | ||
| Journal of AI and Data Mining | ||
| دوره 8، شماره 4، بهمن 2020، صفحه 515-523 اصل مقاله (1.06 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2020.9021.2038 | ||
| نویسندگان | ||
| J. Tayyebi1؛ E. Hosseinzadeh* 2 | ||
| 1Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran. | ||
| 2Department of Mathematics, Kosar University of Bojnord, Bojnord, Iran. | ||
| چکیده | ||
| The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is presented to cluster incomplete fuzzy data. The method substitutes missing attribute by a trapezoidal fuzzy number to be determined by using the corresponding attribute of q nearest-neighbor. Comparisons and analysis of the experimental results demonstrate the capability of the proposed method. | ||
| کلیدواژهها | ||
| Fuzzy c-means algorithm؛ Incomplete data؛ Fuzzy data؛ Ranking function | ||
| مراجع | ||
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