TECM: Transfer Learning-based Evidential C-Means Clustering
This work addresses data sensitivity issues in evidential clustering for domains like pattern recognition, but it is incremental as it builds on existing ECM and transfer learning methods.
The paper tackles the problem of evidential c-means (ECM) clustering being sensitive to insufficient or contaminated data by proposing TECM, a transfer learning-based ECM algorithm that integrates knowledge from a source domain to improve clustering performance in a target domain, with experiments showing its effectiveness compared to ECM and other algorithms.
As a representative evidential clustering algorithm, evidential c-means (ECM) provides a deeper insight into the data by allowing an object to belong not only to a single class, but also to any subset of a collection of classes, which generalizes the hard, fuzzy, possibilistic, and rough partitions. However, compared with other partition-based algorithms, ECM must estimate numerous additional parameters, and thus insufficient or contaminated data will have a greater influence on its clustering performance. To solve this problem, in this study, a transfer learning-based ECM (TECM) algorithm is proposed by introducing the strategy of transfer learning into the process of evidential clustering. The TECM objective function is constructed by integrating the knowledge learned from the source domain with the data in the target domain to cluster the target data. Subsequently, an alternate optimization scheme is developed to solve the constraint objective function of the TECM algorithm. The proposed TECM algorithm is applicable to cases where the source and target domains have the same or different numbers of clusters. A series of experiments were conducted on both synthetic and real datasets, and the experimental results demonstrated the effectiveness of the proposed TECM algorithm compared to ECM and other representative multitask or transfer-clustering algorithms.