An improved apriori algorithm based on support weight matrix for data mining in transaction database

  • Li-na SunEmail author
Original Research


Data mining is a process to discover hidden information or knowledge automatically from huge database. In order to reduce the number of scanning databases and reflect the importance of different items and transaction so as to extract more valuable information, an improved Apriori algorithm is proposed in this paper, which is to build the 0–1 transaction matrix by scanning transaction database for getting the weighted support and confidence. The items and transactions is weighted to reflect the importance in the transaction database. The experiment results, both qualitative and quantitative, have shown that our improved algorithm shortens the running time and reduces the memory requirement and the number of I/O operations. Meanwhile, the support for rare items tends to increase, while the support for other items decreases slightly, thus the hidden and valuable items can be effectively extracted.


Data mining Apriori algorithm Weight matrix Support and confidence k-Itemset 



This work was financially supported by National natural science foundation (No. 11671119); The Scientific and Technological Research Program of Henan Province, China (No. 172102210111); The Scientific and Technological Research Program of Henan Province China (No. 172102210441).


  1. Asif M, Jamil A (2015) Analysis of effectiveness of apriori and frequent pattern tree algorithm in software engineering data mining. In: Proceedings of 2015 6th international conference on intelligent systems, modelling and simulation, Shanghai, pp 28–33Google Scholar
  2. Baralis E, Caglicro I, Cerquitclli T et al (2014) NEM7C0; Mining network data through cloud-based data mining techniques. In: Utility and cloud computing, 2014 IEEE/ACM 7th international conference on IEEE, vol 201, pp 503–504Google Scholar
  3. Bellogín A, Castells P, Cantador I (2013) Improving memory-based collaborative filtering by neighbour selection based on user preference overlap. In: Proceedings of the 10th conference on open research areas in information retrieval, pp 145–148Google Scholar
  4. Bhandari A, Gupta A, Das D (2015) Improvised Apriori Algorithm using frequent pattern tree for real time applications in data mining. Proc Comput Sci 46:644–651CrossRefGoogle Scholar
  5. Dong J, Han M (2007) BitTableFI: an efficient mining frequent itemsets algorithm. Knowl Based Syst 20(4):329–335MathSciNetCrossRefGoogle Scholar
  6. Fu S, Zhou HJ (2013) The research and improvement of apriori algorithm for mining association rules. Microelectron Comput 9:110–114Google Scholar
  7. Huang Chuanguang Y, Jian W, Jing et al (2010) Research on collaborative filtering recommendation algorithm for indefinite neighbors. Comput Sci 33(8):1369–1377Google Scholar
  8. Jiao Y (2013) Research of an improved apriori algorithm in data mining association rules. Int J Comput Commun Eng 2(1):25–27Google Scholar
  9. Kais Dai, Ana Fernández V (2018) The workforce analyzer: group discovery among LinkedIn public profiles. J Ambient Intell Hum Comput 9(6):2025–2034CrossRefGoogle Scholar
  10. Lazcorreta E, Botella F, Fernández-Caballero A (2008) Towards personalized recommendation by two-step modified Apriori data mining algorithm. Expert Syst Appl 35(3):1422–1429CrossRefGoogle Scholar
  11. Liao J, Ghao Y, Long S (2014) MRPrePost-A parallel algorithm adapted for miningbig data. In: 2014 IEEE workshop on electronics, computer and applications, IEEE, 564–568Google Scholar
  12. Park JS, Chen MS, Yu PS (1997) Using a hash-based method with transaction trimming and database scan reduction for mining association rules. IEEE Trans Knowl Data Eng 9(5):813–825CrossRefGoogle Scholar
  13. Park DH, Kim HK, Choi IY et al (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072CrossRefGoogle Scholar
  14. Rao S, Gupta R (2012) Implementing improved algorithm over APRIORI data mining association rule algorithm. Int J Comput Sci Technol 3(1):489–493Google Scholar
  15. Soltani A, Akbarzadcht MR (2014) Confabulation-inspired association rule mining for rare and frequent itemsets. IEEE Trans Neural Netw Learn Syst 25(11):83–92CrossRefGoogle Scholar
  16. Toivonen H (1996) Sampling large databases for association rules. In: Proceedings of the 22th international conference on very large databases (VLDB’96). Morgan Kaufmann, Mumbai, India, pp 134–145Google Scholar
  17. Wong J, Chung Y (2008) Comparison of methodology approach to identify causal factors of accident severity. Transp Res Rec 2083:190–198CrossRefGoogle Scholar
  18. Zaki MJ (1999) Parallel and distributed association mining: a survey. IEEE Concurr Spec Issue Parallel Mech Data Min 7(4):14–25Google Scholar
  19. Zhang S, Du Z, Wang JTL (2015) New techniques for mining frequent patterns in unordered trees. IEEE Trans Cybern 45(6):1113–1125CrossRefGoogle Scholar
  20. Zhao BG, Liu Y (2015) An efficient Bittable Based frequent itemsets mining algorithm. J Shandong Univ 2015(5):23–29Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Henan University Minsheng CollegeKaifengChina

Personalised recommendations