Li, Chang-Tsun and Anand, Sarabjot Singh (2007) DIVA : A variance-based clustering approach for multi-type relational data. In: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management (CIKM'07), Lisbon, Portual, 6-9 Nov 2007. Published in: Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management (CIKM'07) pp. 147-156. ISBN 9781595938039. doi:10.1145/1321440.1321463

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Abstract

Clustering is a common technique used to extract knowledge from a dataset in unsupervised learning. In contrast to classical propositional approaches that only focus on simple and flat datasets, relational clustering can handle multi-type interrelated data objects directly and adopt semantic information hidden in the linkage structure to improve the clustering result. However, exploring linkage information will greatly reduce the scalability of relational clustering. Moreover, some characteristics of vector data space utilized to accelerate the propositional clustering procedure are no longer valid in relational data space. These two disadvantages restrain the relational clustering techniques from being applied to very large datasets or in time-critical tasks, such as online recommender systems. In this paper we propose a new variance-based clustering algorithm to address the above difficulties. Our algorithm combines the advantages of divisive and agglomerative clustering paradigms to improve the quality of cluster results. By adopting the idea of Representative Object, it can be executed with linear time complexity. Experimental results show our algorithm achieves high accuracy, efficiency and robustness in comparison with some well-known relational clustering approaches.

Item Type:	Conference Item (Paper)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title:	Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management (CIKM'07)
Publisher:	ACM
ISBN:	9781595938039
Official Date:	11 May 2007
Dates:	Date Event 11 May 2007 Published
Page Range:	pp. 147-156
DOI:	10.1145/1321440.1321463
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	27 December 2015
Conference Paper Type:	Paper
Title of Event:	Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management (CIKM'07)
Type of Event:	Conference
Location of Event:	Lisbon, Portual
Date(s) of Event:	6-9 Nov 2007
Related URLs:	Organisation

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