Directional co-clustering

Aghiles Salah () and Mohamed Nadif ()
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Aghiles Salah: SIS, Singapore Management University
Mohamed Nadif: LIPADE, Paris Descartes University

Advances in Data Analysis and Classification, 2019, vol. 13, issue 3, No 2, 620 pages

Abstract: Abstract Co-clustering addresses the problem of simultaneous clustering of both dimensions of a data matrix. When dealing with high dimensional sparse data, co-clustering turns out to be more beneficial than one-sided clustering even if one is interested in clustering along one dimension only. Aside from being high dimensional and sparse, some datasets, such as document-term matrices, exhibit directional characteristics, and the $$L_2$$ L 2 normalization of such data, so that it lies on the surface of a unit hypersphere, is useful. Popular co-clustering assumptions such as Gaussian or Multinomial are inadequate for this type of data. In this paper, we extend the scope of co-clustering to directional data. We present Diagonal Block Mixture of Von Mises–Fisher distributions (dbmovMFs), a co-clustering model which is well suited for directional data lying on a unit hypersphere. By setting the estimate of the model parameters under the maximum likelihood (ML) and classification ML approaches, we develop a class of EM algorithms for estimating dbmovMFs from data. Extensive experiments, on several real-world datasets, confirm the advantage of our approach and demonstrate the effectiveness of our algorithms.

Keywords: Co-clustering; Directional data; von Mises-Fisher distribution; EM algorithm; Document clustering; Main 62H30; Secondary 62H11 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11634-018-0323-4

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