Extraction of Top K Itemsets From High Utility Itemsets Using Faster High-Utility Itemset Miner

Authors(2) :-M. Geetha, S. Kavitha

Frequent itemset mining is the recent research topic in the data mining systems. It generally composes of tremendous volume of frequently searched/retrieved item with low/ high itemset values. This dilemma doesn’t satisfy the user’s requirements. The utility itemsets is an important topic and it can be measure in terms of weight, value, quantity and all other information’s depending on the user’s requirements. If the utility itemset is no less than user specified min utility, so this itemset is called a utility of high itemset. It contains a many applications like biomedicine, mobile computing, market analysis, etc. In database, the HUI is a difficult, because in FIM used the downward closer property is does not hold the utility of itemsets. Superset the low utility itemset can be a high utility so the3 HUI pruning search space is also difficult. To overcome this issue, we discovered fittest threshold for mining the relevant itemsets from set of itemsets. Setting of min-util value to the user is a daunting task. In order to find an efficient threshold value for the users, the behaviors of the users are studied. In this work, we proposed two mechanisms, namely, mining top k utility itemsets and mining top k utility itemsets in single phase in which k is the number of covered HUI mining. Initially, we give an auxiliary examination of the two calculations with talks on their preferences and restrictions. Exact assessments on both genuine and manufactured datasets demonstrate that the execution of the proposed calculations is near that of the ideal instance of best in class utility mining calculations.

Authors and Affiliations

M. Geetha
Research Scholar, Department of Computer Science, Sakthi college of Arts and Science For Women, oddanchatram, Tamil Nadu, India
S. Kavitha
Head and Associate Professor, Department of Computer Science,Sakthi college of Arts and Science For Women, oddanchatram, Tamil Nadu, India

Cloud computing, Cloud security, Peer to Peer, Resource Description Framework.

  1. Vincent S. Tseng, Cheng-Wei Wu, Philippe Fournier-Viger, and Philip S. Yu, "Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets", IEEE Transactins on Knowledge and Data Engineering, Vol. 27, No. 3, 2015.
  2. Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, ByeongSoo Jeong, and Young-Koo Lee, "Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases ", IEEE Transactions on Knowledge and Data Engineering, Vol.21, No 12, December 2009, pp 1708-1721.
  3. Vincent S. Tseng, Bai-En Shie, Cheng-Wei Wu, and Philip S. Yu, "Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases", IEEE Transactions on Knowledge and Data Engineering, Vol.25, No. 8, AUGUST 2013, pp 1772-1786.
  4. Chun-Jung Chu, Vincent S. Tseng, Tyne Liang, "An efficient algorithm for mining high utility itemsets with negative item values in large databases", Elsevier, 2009. doi:10.1016/j.amc.2009.05.066.
  5. Hua-Fu Li, Hsin-Yun Huang, Suh-Yin Lee, "Fast and memory efficient mining of high-utility itemsets from data streams: with and without negative item profits", Springer, 2010. DOI 10.1007.
  6. Sen Su, Shengzhi Xu, Xiang Cheng, Zhengyi Li, and Fangchun Ya, "Differentially Private Frequent Itemset Mining via Transaction Splitting", IEEE Transactions on Knowledge and Data Engineering, Vol.27, No 7, July 2015
  7. Vincent S. Tseng, Cheng-Wei Wu, Viger, Philip S. Yu," Efficient Algorithms for Mining Top-K High Utility Itemsets", IEEE Transactions on Knowledge and Data Engineering, DOI 10.1109/TKDE.2015.
  8. Alva Erwin, Raj P. Gopalan, and N. R. Achuthan, "Efficient Mining of High Utility Itemsets from Large Datasets", In Proc. of PAKDD 2008.
  9. Shankar, S.; Purusothaman, T.; Jayanthi, S. "Novel algorithm for mining high utility itemsets" International Conference on Computing, Communication and Networking, Dec. 2008.
  10. Raymond Chan; Qiang Yang; Yi-Dong Shen, "Mining high utility itemsets" In Proc. of Third IEEE Int’l Conf. on Data Mining ,November 2003.
  11. Ramaraju, C., Savarimuthu N. "A conditional tree based novel algorithm for high utility itemset mining", International Conference on Data mining, June 2011.
  12. Ying Liu, Wei-keng Liao, Alok Choudhary "A Fast High Utility Itemsets Mining Algorithm" In Proc. of the Utility-Based Data Mining Workshop, 2005.
  13. Adinarayanareddy B ,O Srinivasa Rao, MHM Krishna Prasad, "An Improved UP-GrowthHigh Utility Itemset Mining" International Journal of Computer Applications (0975-8887) Volume 58-No.2, November 2012.
  14. P. Asha, Dr. T. Jebarajan, G. Saranya, "A Survey on Efficient Incremental Algorithm for Mining High Utility Itemsets in Distributed and Dynamic Database" IJETAE Journal, Vol.4, Issue 1, January 2014.
  15. L. Sweeney, "k-anonymity: A model for protecting privacy," Int. J.Uncertainity Fuzziness Knows.-Base Syst., vol. 10, no. 5, pp. 557–570,2002.
  16. Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal. Mining frequent patterns with counting inference. SIGKDD Explorations Newsletter, 2(2):66 75, December 2000.
  17. J. Pei, J. Han, and R. Mao. Closet: An efficient algorithm for mining frequent closed itemsets. In DMKD 00: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pages 21 30, May 2000.
  18. M. J. Zaki and C.-J. Hsiao. Charm: An efficient algorithm for closed itemset mining. In SDM 02: Proceedings of the second SIAM International Conference on Data Mining, April 2002.
  19. K. Gouda and M. J. Zaki. Genmax: An efficient algorithm for mining maximal frequent itemsets. Data Mining and Knowledge Discovovery, 11(3):223 242, 2005.
  20. G. Grahne and J. Zhu. Efficiently using prefix-trees in mining frequent itemsets. In FIMI 03: Proceedings of the ICDM 2003.Workshop on Frequent Itemset Mining Implementations, November 2003.
  21. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. Int. Conf. Very Large Data Bases, 1994, pp. 487–499.
  22. C. Ahmed, S. Tanbeer, B. Jeong, and Y. Lee, "Efficient tree structures for high-utility pattern mining in incremental databases," IEEE Trans. Knowl. Data Eng., vol. 21, no. 12, pp. 1708–1721, Dec. 2009.
  23. K. Chuang, J. Huang, and M. Chen, "Mining top-k frequent patterns in the presence of the memory constraint," VLDB J., vol. 17, pp. 1321–1344, 2008.
  24. R. Chan, Q. Yang, and Y. Shen, "Mining high-utility itemsets," in Proc. IEEE Int. Conf. Data Mining, 2003, pp. 19–26.
  25. P. Fournier-Viger and V. S. Tseng, "Mining top-k sequential rules," in Proc. Int. Conf. Adv. Data Mining Appl., 2011, pp. 180–194.

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 445-453
Manuscript Number : CSEIT1833194
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

M. Geetha, S. Kavitha, "Extraction of Top K Itemsets From High Utility Itemsets Using Faster High-Utility Itemset Miner", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.445-453, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833194

Article Preview

Follow Us

Contact Us