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Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data
Joint Authors
Gyenesei, Attila
Király, András
Abonyi, János
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-01-30
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Király, András& Gyenesei, Attila& Abonyi, János. 2014. Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1051424
Modern Language Association (MLA)
Király, András…[et al.]. Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data. The Scientific World Journal No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1051424
American Medical Association (AMA)
Király, András& Gyenesei, Attila& Abonyi, János. Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1051424
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051424