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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 3/2016

01.06.2016 | Regular Paper

Co-clustering of multi-view datasets

verfasst von: Syed Fawad Hussain, Shariq Bashir

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2016

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Abstract

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.
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Fußnoten
1
In practice, a sample is used to estimate the value corresponding to \(\rho \,\%\) rather than sorting the values in the entire matrix and removing the lowest values.
 
2
We ignore here the normalization factor for the sake of clarity.
 
3
Values for \(\rho =1\) and \(\lambda =0\) are omitted since they will result in all 0’s (pruning 100 % of similarity values in R) and all 1’s (raising all values in M to the power 0) in the R and M matrices, respectively.
 
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Metadaten
Titel
Co-clustering of multi-view datasets
verfasst von
Syed Fawad Hussain
Shariq Bashir
Publikationsdatum
01.06.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 3/2016
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-015-0861-4

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