A New Approach for Mining Top-Rank-k Erasable Itemsets

  • Giang Nguyen
  • Tuong Le
  • Bay Vo
  • Bac Le
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8397)

Abstract

Erasable itemset mining first introduced in 2009 is an interesting variation of pattern mining. The managers can use the erasable itemsets for planning production plan of the factory. Besides the problem of mining erasable itemsets, the problem of mining top-rank-k erasable itemsets is an interesting and practical problem. In this paper, we first propose a new structure, call dPID_List and two theorems associated with it. Then, an improved algorithm for mining top-rank-k erasable itemsets using dPID_List structure is developed. The effectiveness of the proposed method has been demonstrated by comparisons in terms of mining time and memory usage with VM algorithm for three datasets.

Keywords

data mining erasable itemset pattern mining top-rank-k 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5(6), 914–925 (1993)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB 1994, pp. 487–499 (1994)Google Scholar
  3. 3.
    Deng, Z.H.: Mining top-rank-k erasable itemsets by PID_lists. International Journal of Intelligent Systems 28(4), 366–379 (2013)CrossRefGoogle Scholar
  4. 4.
    Deng, Z.H., Xu, X.R.: Fast mining erasable itemsets using NC_sets. Expert Systems with Applications 39(4), 4453–4463 (2012)CrossRefGoogle Scholar
  5. 5.
    Deng, Z., Fang, G., Wang, Z., Xu, X.: Mining erasable itemsets. In: ICMLC 2009, pp. 67–73 (2009)Google Scholar
  6. 6.
    Deng, Z.H., Xu, X.R.: An efficient algorithm for mining erasable itemsets. In: ACDM 2010, pp. 214–225 (2009)Google Scholar
  7. 7.
    Deng, Z., Xu, X.: Mining top-rank-k erasable itemsets. ICIC Express Letter 2011 5(1), 15–20 (2011)Google Scholar
  8. 8.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD 2000, pp. 1–12 (2000)Google Scholar
  9. 9.
    Le, T., Vo, B., Coenen, F.: An efficient algorithm for mining erasable itemsets using the difference of NC-Sets. In: IEEE SMC 2013, Manchester, UK, pp. 2270–2274 (2013)Google Scholar
  10. 10.
    Le, T., Vo, B.: MEI: An efficient algorithm for mining erasable itemsets. Engineering Applications of Artificial Intelligence 27(1), 155–166 (2014)CrossRefGoogle Scholar
  11. 11.
    Vo, B., Hong, T.-P., Le, B.: DBV-Miner: A dynamic bit-vector approach for fast mining frequent closed itemsets. Expert Systems with Applications 39(8), 7196–7206 (2012)CrossRefGoogle Scholar
  12. 12.
    Vo, B., Le, T., Coenen, F., Hong, T.-P.: A hybrid approach for mining frequent itemsets. In: IEEE SMC 2013, Manchester, UK, pp. 4647–4651 (2013)Google Scholar
  13. 13.
    Vo, B., Le, T., Hong, T.-P., Le, B.: Maintenance of a frequent-itemset lattice based on Pre-large concept. In: KSE 2013, Ha Noi, Vietnam, pp. 295–305 (2013)Google Scholar
  14. 14.
    Zaki, M., Gouda, K.: Fast vertical mining using diffsets. In: SIGKDD 2003, pp. 326–335 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Giang Nguyen
    • 1
  • Tuong Le
    • 2
  • Bay Vo
    • 2
  • Bac Le
    • 3
  1. 1.University of TechnologyHo Chi Minh CityVietnam
  2. 2.Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam
  3. 3.Faculty of Information TechnologyUniversity of ScienceHo Chi Minh CityVietnamz

Personalised recommendations