Abstract
Frequent itemset mining is a fundamental element with respect to many data mining problems directed at finding interesting patterns in data. Recently the PrePost algorithm, a new algorithm for mining frequent itemsets based on the idea of N-lists, which in most cases outperforms other current state-of-the-art algorithms, has been presented. This paper proposes an improved version of PrePost, the N-list and Subsume-based algorithm for mining Frequent Itemsets (NSFI) algorithm that uses a hash table to enhance the process of creating the N-lists associated with 1-itemsets and an improved N-list intersection algorithm. Furthermore, two new theorems are proposed for determining the “subsume index” of frequent 1-itemsets based on the N-list concept. Using the subsume index, NSFI can identify groups of frequent itemsets without determining the N-list associated with them. The experimental results show that NSFI outperforms PrePost in terms of runtime and memory usage and outperforms dEclat in terms of runtime.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
Downloaded from http://fimi.cs.helsinki.fi/data/.
References
Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Proceeding of the SIGMOD’93, pp 207–216
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceeding of the VLDB’94, pp 487–499
Agrawal R, Srikant R (1995) Mining sequential patterns. In: Proceeding of the 11th international conference on data engineering, Taipei, Taiwan, pp 3–14
Ayres J, Gehrke JE, Yiu T, Flannick J (2002) Sequential pattern mining using a bitmap representation. In: Proceeding of the SIGKDD’02, pp 429–435
Baralis E, Cerquitelli T, Chiusano S (2010) Constrained itemset mining on a sequence of incoming data blocks. Int J Intell Syst 25(5):389–410
Chen G, Liu H, Yu L, Wei Q, Zhang X (2006) A new approach to classification based on association rule mining. Decis Support Syst 42(2):674–689
Coenen F, Leng P, Zhang L (2007) The effect of threshold values on association rule based classification accuracy. Data Knowl Eng 60(2):345–360
Deng Z, Fang G, Wang Z, Xu X (2009) Mining erasable itemsets. In: Proceeding of the ICMLC’09, pp 67–73
Deng Z, Wang Z (2010) A new fast vertical method for mining frequent patterns. Int J Comput Intell Syst 3(6):733–744
Deng Z, Wang Z, Jiang JJ (2012) A new algorithm for fast mining frequent itemsets using N-lists. Sci China Inform Sci 55(9):2008–2030
Dong J, Han M (2007) BitTableFI: an efficient mining frequent itemsets algorithm. Knowl Based Syst 20:329–335
Fournier-Viger P, Faghihi U, Nkambou R, Nguifo EM (2012) CMRules: an efficient algorithm for mining sequential rules common to several sequences. Knowl Based Syst 25(1):63–76
Grahne G, Zhu J (2005) Fast algorithms for frequent itemset mining using FP-trees. IEEE Trans Knowl Data Eng 17:1347–1362
Gouda K, Hassaan M, Zaki MJ (2010) PRISM: a primal-encoding approach for frequent sequence mining. J Comput Syst Sci 76(1):88–102
Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceeding of the SIGMODKDD’00, pp 1–12
Le T, Vo B, Coenen F (2013) An efficient algorithm for mining erasable itemsets using the difference of NC-Sets. In: Proceeding of the IEEE SMC’13, pp 2270–2274
Le T, Vo B (2014) MEI: an efficient algorithm for mining erasable itemsets. Eng Appl Artif Intell 27(1):155–166
Li W, Han J, Pei J (2001) CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceeding of the ICDM’01, pp 369–376
Lim AHL, Lee CS (2010) Processing online analytics with classification and association rule mining. Knowl Based Syst 23(3):248–255
Liu B, Hsu W, Ma Y (1998) Integrating classification and association rule mining. In: Proceeding of the SIGKDD’98, pp 80–86
Liu L, Yu Z, Guo J, Mao C, Hong X (2013) Chinese question classification based on question property kernel. Int J Mach Learn Cybern. doi:10.1007/s13042-013-0216-y
Lucchese B, Orlando S, Perego R (2006) Fast and memory efficient mining of frequent closed itemsets. IEEE Trans Knowl Data Eng 18(1):21–36
Mohamed MH, Darwieesh MM (2013) Efficient mining frequent itemsets algorithms. Int J Mach Learn Cybern. doi:10.1007/s13042-013-0172-6
Nguyen LTT, Vo B, Hong TP, Hoang CT (2012) Classification based on association rules: a lattice-based approach. Expert Syst Appl 39(13):11357–11366
Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Efficient mining of association rules using closed itemset lattices. Inform Syst 24(1):25–46
Pham TT, Luo JW, Hong TP, Vo B (2012) MSGPs: a novel algorithm for mining sequential generator patterns. In: Proceeding of the ICCCI’12, pp 393–402
Song W, Yang B, Xu Z (2008) Index-BitTableFI: an improved algorithm for mining frequent itemsets. Knowl Based Syst 21:507–513
Veloso A, Meira W Jr, Goncalves M, Almeida HM, Zaki MJ (2011) Calibrated lazy associative classification. Inform Sci 181(13):2656–2670
Vo B, Coenen F, Le B (2013) A new method for mining Frequent Weighted Itemsets based on WIT-trees. Expert Syst Appl 40(4):1256–1264
Vo B, Hong TP, Le B (2013) A lattice-based approach for mining most generalization association rules. Knowl Based Syst 45:20–30
Vo B, Le B (2011) Interestingness measures for mining association rules: combination between lattice and hash tables. Expert Syst Appl 38(9):11630–11640
Vo B, Le T, Coenen F, Hong TP (2013) A hybrid approach for mining frequent itemsets. In: Proceeding of the IEEE SMC’13, pp 4647–4651
Zaki MJ (2004) Mining non-redundant association rules. Data Min Knowl Disc 9(3):223–248
Zaki MJ, Parthasarathy S, Ogihara M, Li W (1997) New algorithms for fast discovery of association rules. In: Proceeding of KDD’97, pp 283–286
Zaki MJ, Hsiao CJ (2005) Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans Knowl Data Eng 17(4):462–478
Zhang X, Chen G, Wei Q (2011) Building a highly-compact and accurate associative classifier. Appl Intell 34(1):74–86
Zhao S, Tsang ECC, Chen D, Wang XZ (2010) Building a rule-based classifier—a fuzzy-rough set approach. IEEE Trans Knowl Data Eng 22(5):624–638
Acknowledgments
This research was funded by Foundation for Science and Technology Development of Ton Duc Thang University (FOSTECT), website: http://fostect.tdt.edu.vn, under Grant FOSTECT.2014.BR.07.
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper is a expanded version of the paper “A hybrid approach for mining frequent itemsets” [32] presented in IEEE International Conference on Systems, Man, and Cybernetics 2013.
Rights and permissions
About this article
Cite this article
Vo, B., Le, T., Coenen, F. et al. Mining frequent itemsets using the N-list and subsume concepts. Int. J. Mach. Learn. & Cyber. 7, 253–265 (2016). https://doi.org/10.1007/s13042-014-0252-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-014-0252-2