TECM: Transfer Evidential C-means Clustering
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Clustering is widely used in text analysis, natural language processing, image segmentation, and other data mining fields. As a promising clustering algorithm, the evidential c-means (ECM) can provide a deeper insight on the data by allowing an object to belong to several subsets of classes, which extends those of hard, fuzzy, and possibilistic clustering. However, as it needs to estimate much more parameters than the other classical partition-based algorithms, it only works well when the available data is sufficient and of good quality. In order to overcome these shortcomings, this paper proposes a transfer evidential c-means (TECM) algorithm, by introducing the strategy of transfer learning. The objective function of TECM is obtained by introducing barycenters in the source domain on the basis of the objective function of ECM, and the iterative optimization strategy is used to solve the objective function. In addition, the TECM can adapt to situation where the number of clusters in the source domain and the target domain is different. The proposed algorithm has been validated on synthetic and real-world datasets. Experimental results demonstrate the effectiveness of TECM in comparison with the original ECM as well as other representative multitask or transfer clustering algorithms.
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