Cross-guided clustering: Transfer of relevant supervision across domains for improved clustering

Abstract

Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically transferred to a clustering task in a target domain, by providing a relevant supervised partitioning of a dataset from a different source domain. The target clustering is made more meaningful for the human user by trading off intrinsic clustering goodness on the target dataset for alignment with relevant supervised partitions in the source dataset, wherever possible. We propose a cross-guided clustering algorithm that builds on traditional k-means by aligning the target clusters with source partitions. The alignment process makes use of a cross-domain similarity measure that discovers hidden relationships across domains with potentially different vocabularies. Using multiple real-world datasets, we show that our approach improves clustering accuracy significantly over traditional k-means. © 2009 IEEE.