id: 05986675
dt: a
an: 05986675
au: Labiod, Lazhar; Nadif, Mohamed
ti: Co-clustering for binary data with maximum modularity.
so: Lu, Bao-Liang (ed.) et al., Neural information processing. 18th
    international conference, ICONIP 2011, Shanghai, China, November
    13‒17, 2011. Proceedings, Part II. Berlin: Springer (ISBN
    978-3-642-24957-0/pbk). Lecture Notes in Computer Science 7063, 700-708
    (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: modularity; binary data; co-clustering
ci: 
li: doi:10.1007/978-3-642-24958-7_81
ab: Summary: The modularity measure have been recently proposed for graph
    clustering which allows automatic selection of the number of clusters.
    Empirically, higher values of the modularity measure have been shown to
    correlate well with graph clustering. In order to tackle the
    co-clustering problem for binary data, we propose a generalized
    modularity measure and a spectral approximation of the modularity
    matrix. A spectral algorithm maximizing the modularity measure is then
    presented to search for the row and column clusters simultaneously.
    Experimental results are performed on a variety of real-world data sets
    confirming the interest of the use of the modularity.
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