Co-clustering of multi-view datasets

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• 作者：Syed Fawad Hussain ; Shariq Bashir
• 刊名：Knowledge and Information Systems
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：47
• 期：3
• 页码：545-570
• 全文大小：2,154 KB
• 刊物类别：Computer Science
• 刊物主题：Information Systems and Communication Service
  Business Information Systems
  
• 出版者：Springer London
• ISSN：0219-3116
• 卷排序：47

文摘

In many clustering problems, we have access to multiple sources of data representing different aspects of the problem. Each of these data separately represents an association between entities. Multi-view clustering involves integrating clustering information from these heterogeneous sources of data and has been shown to improve results over a single-view clustering. On the other hand, co-clustering has been widely used as a technique to improve clustering results on a single view by exploiting the duality between objects and their attributes. In this paper, we propose a multi-view clustering setting in the context of a co-clustering framework. Our underlying assumption is that similarity values generated from the individual data can be transferred from one view to the other(s) resulting in a better clustering of the data. We provide empirical evidence to show that this framework results in a better clustering accuracy than those obtained from any of the single views, tested on different datasets.KeywordsMulti-view clusteringEnsemble clusteringSimilarity measureTransfer learningCo-clustering