title: Bit-table based biclustering and frequent closed itemset mining in high-dimensional binary data.
abstract: During the last decade various algorithms have been developed and proposed for discovering overlapping clusters in high-dimensional data. The two most prominent application fields in this research, proposed independently, are frequent itemset mining (developed for market basket data) and biclustering (applied to gene expression data analysis). The common limitation of both methodologies is the limited applicability for very large binary data sets. In this paper we propose a novel and efficient method to find both frequent closed itemsets and biclusters in high-dimensional binary data. The method is based on simple but very powerful matrix and vector multiplication approaches that ensure that all patterns can be discovered in a fast manner. The proposed algorithm has been implemented in the commonly used MATLAB environment and freely available for researchers.
authors.affiliation: Department of Process Engineering, University of Pannonia, Veszprém 8200, Hungary.
authors.forename: András
authors.initials: A
authors.lastname: Király
authors.affiliation: Bioinformatics & Scientific Computing Core, Campus Science Support Facilities, Vienna Biocenter, 1030 Vienna, Austria.
authors.forename: Attila
authors.identifier: 0000-0003-2925-9342
authors.initials: A
authors.lastname: Gyenesei
authors.affiliation: Department of Process Engineering, University of Pannonia, Veszprém 8200, Hungary.
authors.forename: János
authors.initials: J
authors.lastname: Abonyi
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*