Advertisement

An Efficient Mining Algorithm of Closed Frequent Itemsets on Multi-core Processor

  • Huan PhanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

In this paper, we improved a sequential NOV-CFI algorithm mining closed frequent itemsets in transaction databases, called SEQ-CFI and consisting of three phases: the first phase, quickly detect a Kernel_COOC array of co-occurrences and occurrences of kernel item in at least one transaction; the second phase, we built the list of nLOOC-Tree base on the Kernel_COOC and a binary matrix of dataset (self-reduced search space); the last phase, the algorithm is a fast mining closed frequent itemsets base on nLOOC-Tree. The next step, we develop a sequential algorithm for mining closed frequent itemsets and thus parallelize the sequential algorithm to effectively demonstrate the multi-core processor, called NPA-CFI. The experimental results show that the proposed algorithms perform better than other existing algorithms, as well as to expand the parallel NPA-CFI algorithm on distributed computing systems such as Hadoop, Spark.

Keywords

Co-occurrence items Closed frequent itemsets Multi-core processor Parallel NPA-CFI algorithm 

References

  1. 1.
    Agrawal, R., Imilienski, T., Swami, A.: Mining association rules between sets of large databases, pp. 207–216. ACM SIGMOD International Conference on Management of Data, Washington, DC (1993)Google Scholar
  2. 2.
    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: 7th International Conference on Database Theory, pp. 398–416 (1999)Google Scholar
  3. 3.
    Zaki, M. J., Hsiao, C.: CHARM: An efficient algorithm for closed association rule mining. In: 2nd SIAM International Conference on Data Mining, pp. 457–473 (2002)Google Scholar
  4. 4.
    Wang, J., Han, J., Pei, J.: CLOSET+: searching for the best strategies for mining frequent closed itemsets. In: ACM International Conference on Knowledge Disc and Data Mining, pp. 236–245 (2003)Google Scholar
  5. 5.
    Binesh, N., Amiya, K.T.: Accelerating closed frequent itemset mining by elimination of null transactions. J. JETCIS 2(7), 317–324 (2011)Google Scholar
  6. 6.
    Wang, S., Yang, Y., Gao, Y., Chen, G., Zhang, Y.: MapReduce-based closed frequent itemset mining with efficient redundancy filtering. In: IEEE 12th ICDM, pp. 449–453 (2012)Google Scholar
  7. 7.
    Wang, F., Yuan, B.: Parallel frequent pattern mining without candidate generation on GPUs, pp. 1046–1052. IEEE ICDM Workshop, Shenzhen (2014)Google Scholar
  8. 8.
    Prabha, S., Shanmugapriya, S., Duraiswamy, K.: A survey on closed frequent pattern mining. Int. J. Comput. Appl. 63(14), 47–52 (2013)Google Scholar
  9. 9.
    Huan P.: NOV-CFI: a novel algorithm for closed frequent itemsets mining in transactional databases. In: ICNCC 2018, pp. 58–63. ACM, NY (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Division of ITUniversity of Social Sciences and Humanities – VNU-HCMCHo Chi Minh CityVietnam
  2. 2.Faculty of Mathematics and Computer ScienceUniversity of Science – VNU-HCMCHo Chi Minh CityVietnam

Personalised recommendations