Transfer prototype-based fuzzy clustering

Traditional prototype-based clustering methods, such as the well-known fuzzy c-means (FCM) algorithm, usually need sufficient data to find a good clustering partition. If available data are limited or scarce, most of them are no longer effective. While the data for the current clustering task may be scarce, there is usually some useful knowledge available in the related scenes/domains. In this study, the concept of transfer learning is applied to prototypebased fuzzy clustering (PFC). Specifically, the idea of leveraging knowledge from the source domain is exploited to develop a set of transfer PFC algorithms. First, two representative PFC algorithms, namely, FCM and fuzzy subspace clustering, have been chosen to incorporate with knowledge leveraging mechanisms to develop the corresponding transfer clustering algorithms based on an assumption that there are the same number of clusters between the target domain (current scene) and the source domain (related scene). Furthermore, two extended versions are also proposed to implement the transfer learning for the situation that there are different numbers of clusters between two domains. The novel objective functions are proposed to integrate the knowledge from the source domain with the data in the target domain for the clustering in the target domain. The proposed algorithms have been validated on different synthetic and real-world datasets. Experimental results demonstrate their effectiveness in comparison with both the original PFC algorithms and the related clustering algorithms like multitask clustering and coclustering.