document: Fully automatic cross-associations Large , sparse binary matrices arise in numerous data mining applications , such as the analysis of market baskets , web graphs , social networks , co-citations , as well as information retrieval , collaborative filtering , sparse matrix reordering , etc. . Virtually all popular methods for the analysis of such matrices -- e.g. , k-means clustering , METIS graph partitioning , SVD\/PCA and frequent itemset mining -- require the user to specify various parameters , such as the number of clusters , number of principal components , number of partitions , and `` support . '' Choosing suitable values for such parameters is a challenging problem.Cross-association is a joint decomposition of a binary matrix into disjoint row and column groups such that the rectangular intersections of groups are homogeneous . Starting from first principles , we furnish a clear , information-theoretic criterion to choose a good cross-association as well as its parameters , namely , the number of row and column groups . We provide scalable algorithms to approach the optimal . Our algorithm is parameter-free , and requires no user intervention . In practice it scales linearly with the problem size , and is thus applicable to very large matrices . Finally , we present experiments on multiple synthetic and real-life datasets , where our method gives high-quality , intuitive results .
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