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Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks

Bhattacharya, Indrajit and Godbole, Shantanu and Joshi, Sachindra and Verma, Ashish (2012) Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks. In: ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 6 (2).

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Official URL: http://dx.doi.org/10.1145/2297456.2297461

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 for clustering a target task, by providing a relevant supervised partitioning of a dataset from a different source task. The target clustering is made more meaningful for the human user by trading-off intrinsic clustering goodness on the target task for alignment with relevant supervised partitions in the source task, 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-task similarity measure that discovers hidden relationships across tasks. When the source and target tasks correspond to different domains with potentially different vocabularies, we propose a projection approach using pivot vocabularies for the cross-domain similarity measure. Using multiple real-world and synthetic datasets, we show that our approach improves clustering accuracy significantly over traditional k-means and state-of-the-art semi-supervised clustering baselines, over a wide range of data characteristics and parameter settings.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to the ACM
Keywords: Multitask; transfer; cluster alignment
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 17 Sep 2012 10:34
Last Modified: 17 Sep 2012 10:34
URI: http://eprints.iisc.ernet.in/id/eprint/45067

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