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- W3146027516 abstract "Semi-supervised clustering is a constrained clustering technique that organizes a collection of unlabeled data into homogeneous subgroups with the help of domain knowledge expressed as constraints. These methods are, most of the time, variants of the popular k-means clustering algorithm. As such, they are based on a criterion to minimize. Amongst existing semi-supervised clusterings, Semi-supervised Evidential Clustering (SECM) deals with the problem of uncertain/imprecise labels and creates a credal partition. In this work, a new heuristic algorithm, called SECM-h, is presented. The proposed algorithm relaxes the constraints of SECM in such a way that the optimization problem is solved using the Lagrangian method. Experimental results show that the proposed algorithm largely improves execution time while accuracy is maintained." @default.
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- W3146027516 date "2021-06-01" @default.
- W3146027516 modified "2023-10-06" @default.
- W3146027516 title "Fast semi-supervised evidential clustering" @default.
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- W3146027516 doi "https://doi.org/10.1016/j.ijar.2021.03.008" @default.
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