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.
Fatehi, K., Bozorgi, A., Zahedi, M. S., & Asgarian, E. (2014). Improving Semi-supervised Constrained k-Means Clustering Method Using User Feedback. Journal of Computing and Security, 1(4), 273-261.
MLA
Kavan Fatehi; Arastoo Bozorgi; Mohammad Sadegh Zahedi; Ehsan Asgarian. "Improving Semi-supervised Constrained k-Means Clustering Method Using User Feedback". Journal of Computing and Security, 1, 4, 2014, 273-261.
HARVARD
Fatehi, K., Bozorgi, A., Zahedi, M. S., Asgarian, E. (2014). 'Improving Semi-supervised Constrained k-Means Clustering Method Using User Feedback', Journal of Computing and Security, 1(4), pp. 273-261.
VANCOUVER
Fatehi, K., Bozorgi, A., Zahedi, M. S., Asgarian, E. Improving Semi-supervised Constrained k-Means Clustering Method Using User Feedback. Journal of Computing and Security, 2014; 1(4): 273-261.
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