Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data
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 biclusterin...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/870406 |
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| author | András Király Attila Gyenesei János Abonyi |
| author_facet | András Király Attila Gyenesei János Abonyi |
| author_sort | András Király |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-22452c17e5c14009acd3b2744f9f55ae |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-22452c17e5c14009acd3b2744f9f55ae2025-02-03T05:43:49ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/870406870406Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary DataAndrás Király0Attila Gyenesei1János Abonyi2Department of Process Engineering, University of Pannonia, Veszprém 8200, HungaryBioinformatics & Scientific Computing Core, Campus Science Support Facilities, Vienna Biocenter, 1030 Vienna, AustriaDepartment of Process Engineering, University of Pannonia, Veszprém 8200, HungaryDuring 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.http://dx.doi.org/10.1155/2014/870406 |
| spellingShingle | András Király Attila Gyenesei János Abonyi Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data The Scientific World Journal |
| title | Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data |
| title_full | Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data |
| title_fullStr | Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data |
| title_full_unstemmed | Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data |
| title_short | Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data |
| title_sort | bit table based biclustering and frequent closed itemset mining in high dimensional binary data |
| url | http://dx.doi.org/10.1155/2014/870406 |
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