Bit-table based biclustering and frequent closed itemset mining in high-dimensional binary data

András Király, Attila Gyenesei, János Abonyi

Research output: Contribution to journalArticle

5 Citations (Scopus)


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.

Original languageEnglish
Article number870406
JournalThe Scientific World Journal
Publication statusPublished - Mar 6 2014


ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)

Cite this