Improving Semi-supervised Constrained k-Means Clustering Method Using User Feedback
محل انتشار: مجله محاسبات و امنیت، دوره: 1، شماره: 4
سال انتشار: 1393
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 219
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شناسه ملی سند علمی:
JR_JCSE-1-4_002
تاریخ نمایه سازی: 12 دی 1400
چکیده مقاله:
Recently, semi-supervised clustering methods have been considered by many researchers. In this type of clustering, there are some constraints and information about a small portion of data. In constrained k-means method, the user (i.e. an expert) selects the initial seeds. In this paper, a constraint k-means method based on user feedback is proposed. With the help of the user, some initial seeds of boundary data obtained from clustering were selected and then the results of the user feedback were given to the constrained k-means algorithm in order to obtain the most appropriate clustering model for the existing data. The presented method was applied to various standard datasets and the results showed that this method clustered the data with more accuracy than other similar methods.
کلیدواژه ها:
نویسندگان
Kavan Fatehi
University of Yazd
Arastoo Bozorgi
University of Shahid Beheshti
Mohammad Sadegh Zahedi
University of Tehran
Ehsan Asgarian
Quchan Institute of Engineering and Technology