Scalable and efficient method for mining association rules

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Association rules mining (ARM) algorithms have been extensively researched in the last decade. Therefore, numerous algorithms were proposed to discover frequent itemsets and then mine association rules. This paper will present an efficient ARM algorithm by proposing a new technique to generate association rules from a huge set of items, which depends on the concepts of clustering and graph data structure, this new algorithm will be named clustering and graph-based rule mining (CGAR). The CGAR method is to create a cluster table by scanning the database only once, and then clustering the transactions into clusters according to their length. The frequent 1-itemsets will be extracted directly by scanning the cluster table. To obtain frequent kitemsets, where k ≥ 2, we build directed graphs for each cluster in the case of very huge amount of transactions. This approach reduces main memory requirement since it considers only a small cluster at a time and hence it is scalable for any large size of the database. Experiments show that our algorithm outperforms other rule mining algorithms.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009
Pages36-41
Number of pages6
Volume1
DOIs
Publication statusPublished - 2009
Event2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009 - Selangor
Duration: 5 Aug 20097 Aug 2009

Other

Other2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009
CitySelangor
Period5/8/097/8/09

Fingerprint

Association rules
Scanning
Directed graphs
Data structures
Data storage equipment
Experiments

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

AlZoubi, W. A., Abu Bakar, A., & Omar, K. (2009). Scalable and efficient method for mining association rules. In Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009 (Vol. 1, pp. 36-41). [5254819] https://doi.org/10.1109/ICEEI.2009.5254819

Scalable and efficient method for mining association rules. / AlZoubi, Wael A.; Abu Bakar, Azuraliza; Omar, Khairuddin.

Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 1 2009. p. 36-41 5254819.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

AlZoubi, WA, Abu Bakar, A & Omar, K 2009, Scalable and efficient method for mining association rules. in Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. vol. 1, 5254819, pp. 36-41, 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009, Selangor, 5/8/09. https://doi.org/10.1109/ICEEI.2009.5254819
AlZoubi WA, Abu Bakar A, Omar K. Scalable and efficient method for mining association rules. In Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 1. 2009. p. 36-41. 5254819 https://doi.org/10.1109/ICEEI.2009.5254819
AlZoubi, Wael A. ; Abu Bakar, Azuraliza ; Omar, Khairuddin. / Scalable and efficient method for mining association rules. Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 1 2009. pp. 36-41
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