Towards scalable algorithm for closed itemset mining in high-dimensional data
Mining frequent itemsets from large dataset has a major drawback in which the explosive number of itemsets requires additional mining process which might filter the interesting ones. Therefore, as the solution, the concept of closed frequent itemset was introduced that is lossless and condensed repr...
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Institute of Advanced Engineering and Science
2017
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| Online Access: | http://irep.iium.edu.my/63096/ http://irep.iium.edu.my/63096/ http://irep.iium.edu.my/63096/ http://irep.iium.edu.my/63096/1/63096_Towards%20scalable%20algorithm%20for%20closed%20itemset%20_article.pdf http://irep.iium.edu.my/63096/2/63096_Towards%20scalable%20algorithm%20for%20closed%20itemset%20_scopus.pdf |
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iium-630962018-04-18T08:05:27Z http://irep.iium.edu.my/63096/ Towards scalable algorithm for closed itemset mining in high-dimensional data Md. Zaki, Fatimah Audah Zulkurnain, Nurul Fariza TK Electrical engineering. Electronics Nuclear engineering Mining frequent itemsets from large dataset has a major drawback in which the explosive number of itemsets requires additional mining process which might filter the interesting ones. Therefore, as the solution, the concept of closed frequent itemset was introduced that is lossless and condensed representation of all the frequent itemsets and their corresponding supports. Unfortunately, many algorithms are not memory-efficient since it requires the storage of closed itemsets in main memory for duplication checks. This paper presents BFF, a scalable algorithm for discovering closed frequent itemsets from high-dimensional data. Unlike many well-known algorithms, BFF traverses the search tree in breadth-first manner resulted to a minimum use of memory and less running time. The tests conducted on a number of microarray datasets show that the performance of this algorithm improved significantly as the support threshold decreases which is crucial in generating more interesting rules. Institute of Advanced Engineering and Science 2017-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/63096/1/63096_Towards%20scalable%20algorithm%20for%20closed%20itemset%20_article.pdf application/pdf en http://irep.iium.edu.my/63096/2/63096_Towards%20scalable%20algorithm%20for%20closed%20itemset%20_scopus.pdf Md. Zaki, Fatimah Audah and Zulkurnain, Nurul Fariza (2017) Towards scalable algorithm for closed itemset mining in high-dimensional data. Indonesian Journal of Electrical Engineering and Computer Science, 8 (2). pp. 487-494. E-ISSN 2502-4752 http://www.iaescore.com/journals/index.php/IJEECS/article/view/10019/7653 10.11591/ijeecs.v8.i2.pp487-494 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English English |
| topic |
TK Electrical engineering. Electronics Nuclear engineering |
| spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Md. Zaki, Fatimah Audah Zulkurnain, Nurul Fariza Towards scalable algorithm for closed itemset mining in high-dimensional data |
| description |
Mining frequent itemsets from large dataset has a major drawback in which the explosive number of itemsets requires additional mining process which might filter the interesting ones. Therefore, as the solution, the concept of closed frequent itemset was introduced that is lossless and condensed representation of all the frequent itemsets and their corresponding supports. Unfortunately, many algorithms are not memory-efficient since it requires the storage of closed itemsets in main memory for duplication checks. This paper presents BFF, a scalable algorithm for discovering closed frequent itemsets from high-dimensional data. Unlike many well-known algorithms, BFF traverses the search tree in breadth-first manner resulted to a minimum use of memory and less running time. The tests conducted on a number of microarray datasets show that the performance of this algorithm improved significantly as the support threshold decreases which is crucial in generating more interesting rules. |
| format |
Article |
| author |
Md. Zaki, Fatimah Audah Zulkurnain, Nurul Fariza |
| author_facet |
Md. Zaki, Fatimah Audah Zulkurnain, Nurul Fariza |
| author_sort |
Md. Zaki, Fatimah Audah |
| title |
Towards scalable algorithm for closed itemset mining in high-dimensional data |
| title_short |
Towards scalable algorithm for closed itemset mining in high-dimensional data |
| title_full |
Towards scalable algorithm for closed itemset mining in high-dimensional data |
| title_fullStr |
Towards scalable algorithm for closed itemset mining in high-dimensional data |
| title_full_unstemmed |
Towards scalable algorithm for closed itemset mining in high-dimensional data |
| title_sort |
towards scalable algorithm for closed itemset mining in high-dimensional data |
| publisher |
Institute of Advanced Engineering and Science |
| publishDate |
2017 |
| url |
http://irep.iium.edu.my/63096/ http://irep.iium.edu.my/63096/ http://irep.iium.edu.my/63096/ http://irep.iium.edu.my/63096/1/63096_Towards%20scalable%20algorithm%20for%20closed%20itemset%20_article.pdf http://irep.iium.edu.my/63096/2/63096_Towards%20scalable%20algorithm%20for%20closed%20itemset%20_scopus.pdf |
| first_indexed |
2023-09-18T21:29:30Z |
| last_indexed |
2023-09-18T21:29:30Z |
| _version_ |
1777412415974014976 |