  The traditional prototype based clustering methods, such as the well-known
fuzzy c-mean (FCM) algorithm, usually need sufficient data to find a good
clustering partition. If the available data is limited or scarce, most of the
existing prototype based clustering algorithms will no longer be 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 prototype based fuzzy clustering
(PFC). Specifically, the idea of leveraging knowledge from the source domain is
exploited to develop a set of transfer prototype based fuzzy clustering (TPFC)
algorithms. Three prototype based fuzzy clustering algorithms, namely, FCM,
fuzzy k-plane clustering (FKPC) and fuzzy subspace clustering (FSC), have been
chosen to incorporate with knowledge leveraging mechanism to develop the
corresponding transfer clustering algorithms. Novel objective functions are
proposed to integrate the knowledge of source domain with the data of target
domain for clustering in the target domain. The proposed algorithms have been
validated on different synthetic and real-world datasets and the results
demonstrate their effectiveness when compared with both the original prototype
based fuzzy clustering algorithms and the related clustering algorithms like
multi-task clustering and co-clustering.
