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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925583/ https://www.ncbi.nlm.nih.gov/pubmed/24616651 http://dx.doi.org/10.1155/2014/870406 |
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author | Király, András Gyenesei, Attila Abonyi, János |
author_facet | Király, András Gyenesei, Attila Abonyi, János |
author_sort | Király, András |
collection | PubMed |
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 | Online Article Text |
id | pubmed-3925583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-39255832014-03-10 Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data Király, András Gyenesei, Attila Abonyi, János ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014-01-30 /pmc/articles/PMC3925583/ /pubmed/24616651 http://dx.doi.org/10.1155/2014/870406 Text en Copyright © 2014 András Király et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Király, András Gyenesei, Attila Abonyi, János Bit-Table Based Biclustering and Frequent Closed Itemset Mining in High-Dimensional Binary Data |
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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3925583/ https://www.ncbi.nlm.nih.gov/pubmed/24616651 http://dx.doi.org/10.1155/2014/870406 |
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