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A Survey on Improved Algorithms for Mining Association Rules

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Hoda Khanali, Babak Vaziri
10.5120/ijca2017913985

Hoda Khanali and Babak Vaziri. A Survey on Improved Algorithms for Mining Association Rules. International Journal of Computer Applications 165(9):6-11, May 2017. BibTeX

@article{10.5120/ijca2017913985,
	author = {Hoda Khanali and Babak Vaziri},
	title = {A Survey on Improved Algorithms for Mining Association Rules},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2017},
	volume = {165},
	number = {9},
	month = {May},
	year = {2017},
	issn = {0975-8887},
	pages = {6-11},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume165/number9/27599-2017913985},
	doi = {10.5120/ijca2017913985},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Different types of data, needs of users and variety application problems are lead to produce a range of methods to discover patterns and dependent relationships. This application follows a set of association rules according to know which one of set of objects affects on a set of other objects. This association rules predict the occurrence of an object based on the occurrence of other objects. The associative algorithms have the challenge of redundant association rules and patterns, but studying various methods of association rules is expressive that the recent researches focused on solving the challenges of the tree and lattice structures and their compounds about association algorithms. In this paper, the associative algorithms and their function are described, and finally the new improved association algorithms and the proposed solutions to solve these challenges are explained.

References

  1. Geng, L., and Hamilton, H. 2006. Interestingness measures for data mining: A survey. ACM Computing Surveys (CSUR).
  2. Kim, C., Lee, H., Seol, H., and Lee, C. 2011. Identifying core technologies based on technological cross-impacts: An association rule mining (ARM) and analytic network process (ANP) approach. Expert Systems with Applications.
  3. Tan, P., Kumar, V., and Srivastava, J. 2004. Selecting the right objective measure for association analysis. Information Systems.
  4. Luna, J., Romero, J., and Ventura, S. 2013. Grammar-based multi-objective algorithms for mining association rules. Data & Knowledge Engineering.
  5. Han, J., Kamber, M., and Pei, J. 2011. Data mining: concepts and techniques. Elsevier.
  6. Agrawal, R., and Srikant, R. 1994. Fast algorithms for mining association rules. in 20th International Conference on Very Large Data Bases.
  7. Cao, L. 2009. Data mining and multi-agent integration. Springer Science & Business Media.
  8. Han, J., Pei, J., Yin, Y. and Mao, R. 2004. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data mining and knowledge discovery.
  9. Kantardzic, M., 2011. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.
  10. Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. ACM Sigmod Record.
  11. Tanbeer, S., Ahmed, C., Jeong, B., Lee, Y., and Sliding, w. 2009. Sliding window-based frequent pattern mining over data streams. Information sciences.
  12. Zaki, M., Parthasarathy, S., Ogihara, M., and Li, W. 1997. New algorithms for fast discovery of association rules. in 3rd International Conference on Knowledge Discovery and Data Mining (KDD’97).
  13. Song, K., and Lee, K. 2017. Predictability-based collective class association rule mining. Expert Systems with Applications.
  14. Nguyen, D., Nguyen, L., Vo, B., and Pedrycz, W. 2016. Efficient mining of class association rules with the itemset constraint. Knowledge-Based Systems.
  15. Narvekar, M., and Syed, S. 2015. An Optimized Algorithm for Association Rule Mining Using FP Tree. Procedia Computer Science.
  16. Nguyen, D., Nguyen, L., Vo, B., and Hong, T. 2015. A novel method for constrained class association rule mining. Information Sciences.
  17. Nguyen, L., and Nguyen, N. 2015. An improved algorithm for mining class association rules using the difference of Obidsets. Expert Systems with Applications.
  18. Nguyen, L., Vo, B., Hong, T., and Thanh, H. 2013. CAR-Miner: An efficient algorithm for mining class-association rules. Expert Systems with Applications.
  19. Nguyen, D., Vo, B., and Le, B. 2015. CCAR: An efficient method for mining class association rules with itemset constraints. Engineering Applications of Artificial Intelligence.
  20. Hashem, T., Ahmed, C., Samiullah, M., Akther, S., and Jeong, B. 2014. An efficient approach for mining cross-level closed itemsets and minimal association rules using closed itemset lattices. Expert Systems with Applications.
  21. Zaki, M., and Hsiao, C. 2005. Efficient algorithms for mining closed itemsets and their lattice structure. knowledge and data engineering.
  22. Vo, B., and Le, B. 2009. Fast algorithm for mining minimal generators of frequent closed itemsets and their applications. in Computers & Industrial Engineering.
  23. Beiranvand, V., Mobasher-Kashani, M., and Bakar, A. 2014. Multi-objective PSO algorithm for mining numerical association rules without a priori discretization. Expert Systems with Applications..
  24. Duong, H., Truong, T., and Vo, B. 2014. An efficient method for mining frequent itemsets with double constraints. Engineering Applications of Artificial Intelligence.
  25. Rodríguez-González, A., Martínez-Trinidad, J., Carrasco-Ochoa, J., and Ruiz-Shulcloper, J. 2013. Mining frequent patterns and association rules using similarities. Expert Systems with Applications.
  26. Agrawal, R., and Srikant, R. 1994. Fast algorithms for mining association rules. in 20th int. conf. very large data bases, VLD.
  27. Chen, C., Lan, G., Hong, T., and Lin, Y. 2013. Mining high coherent association rules with consideration of support measure. Expert Systems with Applications.
  28. Vo, B. and Le, B. 2008. A novel classification algorithm based on association rules mining. in InPacific Rim Knowledge Acquisition Workshop.

Keywords

Frequent item sets, Mining association rules, Data mining.