Title: Multimodal Clustering for Community Detection,
Abstract: Multimodal clustering is an unsupervised technique for mining interesting
patterns in $n$-adic binary relations or $n$-mode networks. Among different
types of such generalized patterns one can find biclusters and formal concepts
(maximal bicliques) for 2-mode case, triclusters and triconcepts for 3-mode
case, closed $n$-sets for $n$-mode case, etc. Object-attribute biclustering
(OA-biclustering) for mining large binary datatables (formal contexts or 2-mode
networks) arose by the end of the last decade due to intractability of
computation problems related to formal concepts; this type of patterns was
proposed as a meaningful and scalable approximation of formal concepts. In this
paper, our aim is to present recent advance in OA-biclustering and its
extensions to mining multi-mode communities in SNA setting. We also discuss
connection between clustering coefficients known in SNA community for 1-mode
and 2-mode networks and OA-bicluster density, the main quality measure of an
OA-bicluster. Our experiments with 2-, 3-, and 4-mode large real-world networks
show that this type of patterns is suitable for community detection in
multi-mode cases within reasonable time even though the number of corresponding
$n$-cliques is still unknown due to computation difficulties. An interpretation
of OA-biclusters for 1-mode networks is provided as well.