Occupancy Based Pattern Mining: Current Status And Future Directions

##plugins.themes.academic_pro.article.sidebar##

Published Mar 1, 2020
https://doi.org/10.47164/ijngc.v11i1.171
Download
PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 11, Issue 1, March 2020

##plugins.themes.academic_pro.article.main##

Jhimli Adhikari
Narayan Zantye College, Bicholim, Goa, India

Abstract

The main purpose of data mining and analytics is to find novel, potentially useful patterns that can be utilized in real-world applications to derive beneficial knowledge. In recent years, a new measure of pattern interestingness called occupancy of a pattern was introduced to ensure that each pattern found represents a large part of transactions where it appears. Main objective of this measure is to enhance the quality of a pattern. This article surveys recent studies on pattern mining and its applications based on occupancy. The goal of the paper is to provide both an introduction to occupancy based pattern mining (OPM), and a survey of recent advances and research opportunities. Moreover, main approaches and strategies to solve occupancy based pattern mining problems are also presented. The paper also presents challenges and research opportunities of using occupancy measure in other popular pattern mining problems.

##plugins.themes.academic_pro.article.details##

How to Cite
Jhimli Adhikari. (2020). Occupancy Based Pattern Mining: Current Status And Future Directions. International Journal of Next-Generation Computing, 11(1), 36–51. https://doi.org/10.47164/ijngc.v11i1.171
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Adhikari, J. 2014. Mining calendar-based periodic patterns from nonbinary transactions. Jour- nal of Intelligent Systems Vol.23, No.3.
  2. Agrawal, R., Imielinsk, T., and Swami, A. 1993. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD Conf. Management of Data. pp.207–216.
  3. Agrawal, R. and Srikant, R. 1995. Mining sequential patterns. In Proceedings of the Eleventh
  4. International Conference on Data Engineering, Taipei, Taiwan.
  5. Barbier, G. and Liu, H. 2011. Data mining in social media. In Social Network Data Analytics.
  6. Springer, 327–352.
  7. Burdick, D., Calimlim, M., and Gehrke, J. 2001. Mafia: A maximal frequent itemset algorithm for transactional databases. In Proceedings 17th International Conference on Data Engineering. IEEE.
  8. Cao, L. 2010. Domain-driven data mining: Challenges and prospects. IEEE Trans. Knowl.
  9. Data Eng. Vol.22, No.6, 755–769.
  10. Cao, L. 2013. Combined mining: Analyzing object and pattern relations for discovering and constructing complex yet actionable patterns. Wiley Interdiscip. Rev. Data Min. Knowl. Discov..
  11. Deng, Z. 2020. Mining high occupancy itemsets. Future Gener. Comput. Syst. Vol.102, 222–229.
  12. Gade, K., Wang, J., and Karypis, G. 2004. Efficient closed pattern mining in the pres- ence of tough block constraints. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, USA, August. ACM, 138–147.
  13. Gan, W., Lin, J. C., F.Viger, P., Chao, H., Tseng, V. S., and Yu, P. S. 2018. A survey of utility-oriented pattern mining. CoRR abs/1805.10511.
  14. Gan, W., Lin, J. C., F.Viger, P., Chao, H., Zhan, J., and Zhang, J. 2018. Ex- ploiting highly qualified pattern with frequency and weight occupancy. Knowl. Inf. Syst. Vol.56, No.1, 165–196.
  15. Gan, W., Lin, J. C., F.Viger, P., H.C.Chao, and Yu, P. HUOPM: high utility occupancy pattern mining. IEEE Trans. Cybern. Vol.50, No.3.
  16. Gleich, D. F. and Mahoney, M. W.
  17. Haff, L. R. 1979. An identity for the wishart distribution with applications. Journal of Multi- variate Analysis.
  18. Moens, S., Aksehirli, E., and Goethals, B. 2013. Frequent itemset mining for big data. In
  19. BigData. pp.111–118.
  20. M.S.Chen, Han, J., and P.S.Yu. 1996. Data mining: An overview from a database perspective.
  21. IEEE Transactions on Knowledge and Data Engineering Vol.8, No.6.
  22. Pei, J., Mortazavi, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., and Hsu, M. 2004.
  23. Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Trans. Knowl. Data Eng..
  24. P.F.Viger, P. Lin, J. C., Vo, B.and Chi, T. T. Z., and Le, H. 2017. A survey of itemset mining. WIREs: Data Mining and Knowledge Discovery Vol.7, No.4.
  25. Shen, B., Wen, Z., Zhao, Y., Zhou, D., and Zheng, W. 2016. Ocean: Fast discovery of high utility occupancy itemsets. In Advances in Knowledge Discovery and Data Mining, PAKDD. Springer, 354–365.
  26. Tang, L., Zhang, L., Luo, P., and Wang, M. 2012. Incorporating occupancy into frequent pattern mining for high quality pattern recommendation. In Proc. of the 21st ACM Inter- national Conference on Information and Knowledge Management. ACM, pp.75–84.
  27. Wang, L., Meng, J., Xu, P., and Peng, K. 2018. Mining temporal association rules with frequent itemsets tree. Applied Soft Computing Vol.62, No.
  28. Yao, H., Hamilton, H. J., and Butz, C. J. 2004. A foundational approach to mining itemset utilities from databases. In Proceedings of the Fourth SIAM International Conference on Data Mining. SIAM, 482–486.
  29. Zhang, X., Duan, F., Zhang, L., Cheng, F., Jin, Y., and Tang, K. 2017. Pattern recom- mendation in task-oriented applications: A multi-objective perspective [application notes]. IEEE Comput. Intell. Mag..