An Improved Co-Similarity Measure for Document Clustering

Co-clustering has been defined as a way to organize simultaneously subsets of instances and subsets of features in order to improve the clustering of both of them. In previous work, we proposed an efficient co-similarity measure allowing to simultaneously compute two similarity matrices between obje...

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Bibliographic Details
Published in2010 International Conference on Machine Learning and Applications pp. 190 - 197
Main Authors Hussain, S F, Bisson, G, Grimal, C
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Clustering algorithms
co-clustering
Complexity theory
Equations
Oceans
Sea measurements
Semantics
similarity measure
Strontium
text mining
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Summary:Co-clustering has been defined as a way to organize simultaneously subsets of instances and subsets of features in order to improve the clustering of both of them. In previous work, we proposed an efficient co-similarity measure allowing to simultaneously compute two similarity matrices between objects and features, each built on the basis of the other. Here we propose a generalization of this approach by introducing a notion of pseudo-norm and a pruning algorithm. Our experiments show that this new algorithm significantly improves the accuracy of the results when using either supervised or unsupervised feature selection data and that it outperforms other algorithms on various corpora.
ISBN:1424492114
9781424492114
DOI:10.1109/ICMLA.2010.35