Quantitative temporal association rule mining by genetic algorithm
Abstract
Association rule mining has shown great potential to extract knowledge from multidimensional data sets. However, existing methods in the literature are not effectively applicable to quantitative temporal data. This article extends the concepts of association rule mining from the literature. Based on the extended concepts is presented a method to mine rules from multidimensional temporal quantitative data sets using genetic algorithm, called GTARGA, in reference to Quantitative Temporal Association Rule Mining by Genetic Algorithm. Experiments with QTARGA in four real data sets show that it allows to mine several high-confidence rules in a single execution of the method.
References
[1]
S. Amo, N. A. Silva, R. P. Silva, and F. S. Pereira. Tree pattern mining with tree automata constraints. Information Systems, 35:570-591, 2010.
[2]
B. Catania and A. Maddalena. A unified framework for heterogeneous patterns. Information Systems, 37:460-483, 2012.
[3]
D.-A. Chiang, C.-T. Wang, S.-P. Chen, and C.-C. Chen. The cyclic model analysis on sequential patterns. IEEE Transactions on Knowledge and Data Engineering, 21(11):1617-1628, 2009.
[4]
J. K. Febrer-Hernández and J. Hernández-Palancar. Sequential pattern mining algorithms review. Intelligent Data Analysis, 16:451-466, 2012.
[5]
T. Fu. A review on time series data mining. Engineering Applications of Artificial Intelligence, 24(1):164-181, 2011.
[6]
E. Georgii, L. Richter, U. Rückert, and S. Kramer. Analyzing microarray data using quantitative association rules. Bioinformatics, 21(suppl 2):ii123-ii129, 2005.
[7]
Y. Hai and X. Li. A general temporal association rule frequent itemsets mining algorithm. International Journal of Advancements in Computing Technology, 3(11):63-71, 2011.
[8]
T.-P. Hong, C.-S. Kuo, and S.-C. Chi. Mining association rules from quantitative data. Intelligent Data Analysis, 3(5):363-376, 1999.
[9]
C.-H. Lee, M.-S. Chen, and C.-R. Lin. Progressive partition miner: an efficient algorithm for mining general temporal association rules. IEEE Transaction on Knowledge and Data Engineering, 15(4):1004-1017, 2003.
[10]
Y. J. Lee, J. W. Lee, D. J. Chai, B. H. Hwang, and K. H. Ryu. Mining temporal interval relational rules from temporal data. Journal of Systems and Software, 82(1):155-167, 2009.
[11]
N. R. Mabroukeh and C. I. Ezeife. A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys, 43(1):1-41, 2010.
[12]
D. Martin, A. Rosete, J. Alcala-Fdez, and F. Herrera. A new multiobjective evolutionary algorithm for mining a reduced set of interesting positive and negative quantitative association rules. Evolutionary Computation, IEEE Transactions on, 18(1):54-69, 2014.
[13]
M. Martínez-Ballesteros, A. Troncoso, F. Martínez-Álvarez, and J. C. Riquelme. Mining quantitative association rules based on evolutionary computation and its application to atmospheric pollution. Integrated Computer-Aided Engineering, 17(3):227-242, 2010.
[14]
B. Pei, S. Zhao, H. Chen, X. Zhou, and D. Chen. Farp: Mining fuzzy association rules from a probabilistic quantitative database. Information Sciences, 237:242 - 260, 2013.
[15]
M. Plantevit, A. Laurent, D. Laurent, M. Teisseire, and Y. W. Choong. Mining multidimensional and multilevel sequential patterns. ACM Transactions on Knowledge Discovery from Data, 4(1):1-37, 2010.
[16]
A. Pétrowski. A clearing procedure as a niching method for genetic algorithms. In Proceedings of 3rd IEEE International Conference on Evolutionary Computation, pages 798-803, New York, USA, 1996.
[17]
N. Tatti and B. Cule. Mining closed strict episodes. Data Mining and Knowledge Discovery, 25(1):34-66, 2011.
[18]
X. Yan, C. Zhang, and S. Zhang. Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications, 36:3066-3076, 2009.
[19]
M. Zhang and C. He. Survey on association rules mining algorithms. Advancing Computing, Communication, Control and Management, 56:111-118, 2010.
[20]
H. Zheng, J. He, G. Huang, and Y. Zhang. Optimized fuzzy association rule mining for quantitative data. In 2014 IEEE International Conference on Fuzzy Systems, pages 396-403, Beijing, China, 2014.
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- Fundacao de Amparo a Pesquisa do Estado de Goias: FAPEG
- SBC: Brazilian Computer Society
- Institute of Informatics/Federal University of Goias: INF/UFG
- Faculdades ALFA: Faculdades ALFA
- Global RH Solutions: Global RH Solutions
- Secretaria de Ciencia, Tecnologia e Inovacao do Estado de Goias: SECTEC-GO
- CAPES: Brazilian Higher Education Funding Council
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- SIGAPP: ACM Special Interest Group on Applied Computing
- SIGMIS: ACM Special Interest Group on Management Information Systems
- AIS: Assoc for Information Systems
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Brazilian Computer Society
Porto Alegre, Brazil
Publication History
Published: 26 May 2015
Accepted: 01 April 2015
Received: 01 February 2015
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- Research
- Refereed limited
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SBSI '15
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- Fundacao de Amparo a Pesquisa do Estado de Goias
- SBC
- Institute of Informatics/Federal University of Goias
- Faculdades ALFA
- Global RH Solutions
- Secretaria de Ciencia, Tecnologia e Inovacao do Estado de Goias
- CAPES
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SBSI '15 Paper Acceptance Rate 101 of 313 submissions, 32%;
Overall Acceptance Rate 181 of 557 submissions, 32%
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