A Novel Apriori Algorithm Based on Cross Linker

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 216)

Abstract

Based on the analysis of the Apriori algorithm from the perspectives of time complexity and memory complexity, a novel algorithm was proposed. The proposed algorithm defined a cross-linker structure firstly and then used this structure to substitute the array description of the transactions database. The cross linker structure shortened the length of the objects linked; thus, it reduced the algorithm’s time cost. The cross-linker structure did not save the candidates of frequent itemset also; thus, it reduced the algorithm’s memory cost. The experiments show that the proposed algorithm’s performances are comparable.

Keywords

Apriori algorithm Cross linker Itemset 

Notes

Acknowledgments

This work was supported by the Science and Technology Research Project of Chongqing Education Committee (Grant No. KJ110629).

References

  1. 1.
    Chen G, Zhu Y, Yang H et al (2005) Study of some key techniques in mining association rule. J Comput Res Dev 42(10):1785–1789CrossRefGoogle Scholar
  2. 2.
    Chang R, Liu Z (2011) An improved apriori algorithm. Int Conf Electron Otoelectronics (ICEOE) Daling Liaoning China 11(6):476–478Google Scholar
  3. 3.
    Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM on management of data, vol 28(15). Washington, pp 207–216Google Scholar
  4. 4.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of the 20th international conference VLDB’s 94, vol 9(5). Santiago, Chile, pp 487–499Google Scholar
  5. 5.
    Srikkant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. In: Proceedings of the 1996 ACM SIGMOD conference on management of data, vol 39(26). Montreal, Canada, pp 1–12Google Scholar
  6. 6.
    Honglie Y, Wen J, Wang H et al (2011) An improved apriori algorithm based on the boolean matrix and hadoop. Procedia Eng 15(9):827–831Google Scholar
  7. 7.
    Jiang Y, Wang J (2011) An improved association rules algorithm based on frequent item sets. Procedia Eng 15(9):3335–3340CrossRefGoogle Scholar
  8. 8.
    Buldu Ali, Uogun Kerem (2010) Data mining application on student’s data. Proceida-Soc Behav Sci 25(2):5251–5259CrossRefGoogle Scholar
  9. 9.
    Chu-xiang C, Jiang-jing S, Bing C et al (2011) An improvement apriori arithmetic based on rough set theory. Third Pacific-Asia Conf Circ Commun Syst (PACCS), vol 12(4). Wuhan, China, pp 1–3Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.School of Computer and Information ScienceChongqing Normal UniversityChongqingChina
  2. 2.Computer Engineering DepartmentChongqing Aerospace Polytechnic CollegeChongqingChina

Personalised recommendations