A Review on Efficient Algorithms for Mining Top-K High Utility Item Sets

Authors

  • Reshma Sodanwar  Computer Engineering, SPPU Pune University, Pune, Maharashtra, India
  • Prof. Sachin Bere  Computer Engineering, SPPU Pune University, Pune, Maharashtra, India

Keywords:

Utility mining, high utility itemset mining, top-k pattern mining, top-k high utility itemset mining.

Abstract

High utility itemsets (HUIs) mining is most important topic in data mining.HUI mining means searching all itemsets whose utility is meeting with user-specified minimum utility threshold value i.e min-util. However, Deciding min-util value approximately or exactly is found bit difficult to users, since finding an appropriate minimum utility threshold by trial and error is a tedious as well as time consuming process. Also problem with the same was if threshold value set too low, number of high utility itemset generated were too high,which in turn making the mining process ineffective. Whereas if min-util is set too high, it is likely that very less or no HUIs will be found. The proposed algorithm address the above issues by proposing a new framework for top-k high utility itemset mining, where k is the required number of HUIs to be mined. Two types of efficient algorithms named TKU (mining Top-K Utility itemsets) and TKO (mining Top-K utility itemsets in One phase) are proposed for mining such itemsets without the need to set threshold value by user. System provide comparison of the two algorithms with discussions on their advantages and limitations.

References

  1. R. Agrawal and R. Srikant,\Fast Algorithms for Mining Association Rules,"Proc. 20th Intl Conf. Very L C.F. Ahmed, S.K. Tanbeer, B.-S. Jeong, and Y.-K. Lee,\Efficient Tree Structures
    for High Utility Pattern Mining in Incremental Databases,"IEEE Trans. Knowledge
    and Data Eng., vol. 21, no. 12, pp. 1708-1721, Dec. 2009.
  2. K. Chuang, J. Huang, and M. Chen,\Mining top-k frequent patterns in the presence
    of the memory constraint, "VLDB J., vol. 17, pp. 13211344, 2008.
  3. R. Chan, Q. Yang, and Y. Shen, \Mining high-utility itemsets,"in Proc. IEEE Int.
    Conf. Data Mining, 2003, pp. 1926.
  4. . Fournier-Viger and V. S. Tseng,\Mining top-k sequential rules,"in Proc. Int. Conf.
    Adv. Data Mining Appl., 2011, pp. 180194.
  5. P. Fournier-Viger, C. Wu, and V. S. Tseng,\Mining top-k association rules,"in Proc.
    Int. Conf. Can. Conf. Adv. Artif. Intell., 2012, pp. 6173
  6. P. Fournier-Viger, C. Wu, and V. S. Tseng,\Novel concise representations of high
    utility itemsets using generator patterns,"Iin Proc. Int. Conf. Adv. Data Mining
    Appl. Lecture Notes Comput. Sci., 2014, vol. 8933, pp. 3043
  7. J. Han, J. Pei, and Y. Yin,\Mining frequent patterns without candidate generation,"in Proc. ACM SIGMOD Int. Conf. Manag. Data, 2000, pp. 112.
  8. J. Han, J. Wang, Y. Lu, and P. Tzvetkov,\Mining top-k frequent closed patterns
    without minimum support,"in Proc. IEEE Int. Conf. Data Mining, 2002, pp. 211218.
  9. S. Krishnamoorthy,\LPruning strategies for mining high utility itemsets,"Expert
    Syst. Appl., vol. 42, no. 5, pp. 23712381, 2015.

Downloads

Published

2017-08-31

Issue

Section

Research Articles

How to Cite

[1]
Reshma Sodanwar, Prof. Sachin Bere, " A Review on Efficient Algorithms for Mining Top-K High Utility Item Sets, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.116-120, July-August-2017.