Cross-domain clustering performed by transfer of knowledge across domains

In this paper, we propose a method to improve the results of clustering in a target domain, using significant information from an auxiliary (source) domain dataset. The applicability of this method concerns the field of transfer learning (or domain adaptation), where the performance of a task (say, classification using clustering) in one domain is improved using knowledge obtained from a similar domain. We propose two unsupervised methods of cross-domain clustering and show results on two different categories of benchmark datasets, both having difference in density distributions over the pair of domains. In the first method, we propose an iterative framework, where the clustering in the target domain is influenced by the clusters formed in the source domain and vice-versa. Similarity/dissimilarity measures have been appropriately formulated using Euclidean distance and Bregman Divergence, for cross-domain clustering. In the second method, we perform clustering in the target domain by estimating local density computed using a non-parametric (NP) density estimator (due to less number of samples). Prior to clustering, the NP-density scattering in the target domain is modified using information of cluster density distribution in source domain. Results shown on real-world datasets suggest that the proposed methods of cross-domain clustering are comparable to the recent start-of-the-art work.