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Improving Semi-supervised Constrained k-Means Clustering Method Using User Feedback | ||
| Journal of Computing and Security | ||
| مقاله 2، دوره 1، شماره 4، دی 2014، صفحه 273-261 اصل مقاله (1.03 M) | ||
| نویسندگان | ||
| Kavan Fatehi* 1؛ Arastoo Bozorgi2؛ Mohammad Sadegh Zahedi3؛ Ehsan Asgarian4 | ||
| 1University of Yazd | ||
| 2University of Shahid Beheshti | ||
| 3University of Tehran | ||
| 4Quchan Institute of Engineering and Technology | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Clustering؛ Semi-supervised using user feedback؛ Active Learning؛ Boundary data | ||
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آمار تعداد مشاهده مقاله: 2,053 تعداد دریافت فایل اصل مقاله: 430 |
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