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Review on Rare Itemset Mining

S.Z. Ninoria1 , S.S. Thakur2

Section:Review Paper, Product Type: Journal Paper
Volume-07 , Issue-10 , Page no. 152-157, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7si10.152157

Online published on May 05, 2019

Copyright © S.Z. Ninoria, S.S. Thakur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S.Z. Ninoria, S.S. Thakur, “Review on Rare Itemset Mining,” International Journal of Computer Sciences and Engineering, Vol.07, Issue.10, pp.152-157, 2019.

MLA Style Citation: S.Z. Ninoria, S.S. Thakur "Review on Rare Itemset Mining." International Journal of Computer Sciences and Engineering 07.10 (2019): 152-157.

APA Style Citation: S.Z. Ninoria, S.S. Thakur, (2019). Review on Rare Itemset Mining. International Journal of Computer Sciences and Engineering, 07(10), 152-157.

BibTex Style Citation:
@article{Ninoria_2019,
author = {S.Z. Ninoria, S.S. Thakur},
title = {Review on Rare Itemset Mining},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {07},
Issue = {10},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {152-157},
url = {https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=993},
doi = {https://doi.org/10.26438/ijcse/v7i10.152157}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i10.152157}
UR - https://www.ijcseonline.org/full_spl_paper_view.php?paper_id=993
TI - Review on Rare Itemset Mining
T2 - International Journal of Computer Sciences and Engineering
AU - S.Z. Ninoria, S.S. Thakur
PY - 2019
DA - 2019/05/05
PB - IJCSE, Indore, INDIA
SP - 152-157
IS - 10
VL - 07
SN - 2347-2693
ER -

           

Abstract

These Data mining is the procedure of analyzing unseen patterns of data according to different point of view for classification into useful information, which is collected and gathered in common areas, such as data warehouses, for proficient investigation, data mining algorithms, assisting business decision making and other information requirements to eventually cut costs and raise profits. Data mining is also recognized as data discovery and knowledge discovery. Frequent itemset mining is a significant task in data mining to discover the hidden, interesting associations between items in the database based on the user-specified support and confidence thresholds. Patterns detected by the Association Rule Mining technique are highly useful patterns as are playing vital role in decision making. In this paper we have focused on the other side of the ARM technique which should also get equal emphasis while decision making as the patterns which are not frequent can be more valuable as those are the Rare Itemsets. The fundamental technique of finding the frequent patterns can be used reversely for Rare Itemset Mining. In this paper the brief study of the technique available for Rare Itemset Mining is discussed and explored the utility of the Rare Itemsets in the decision making.

Key-Words / Index Term

Data Mining, Association Rule Mining, Frequent Itemset, Rare Itemset, Minimal Rare Itemset

References

[1] A.Gyenesei,“Mining weighted association rules for fuzzy quantitative items”, InEuropean Conference on Principles of Data Mining and Knowledge Discovery 2000 Sep 13, Springer, Berlin, Heidelberg, pp. 416-423,2000.
[2] A.M.Patel, D.Bhalodiya, “A Survey On Frequent Itemset Mining Techniques Using GPU”,Internatonal Journal Of Innovative Research In Technology,Vol.1,Issue 5,2014.
[3] A.Raorane, R.V. Kulkarni,“Data mining techniques: A source for consumer behavior analysis”, arXiv preprint arXiv:1109.1202. 2011 Sep 6.,2011.
[4] B.Liu, W.Hsu, Y.Ma, “ Mining association rules with multiple minimum supports”, InProceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining 1999 Aug 1, ACM ,pp. 337-341, 1999.
[5] C.K.Chui, B.Kao, E.Hung,“Mining frequent itemsets from uncertain data”,InPacific-Asia Conference on Knowledge Discovery and Data Mining 2007 May 22,Springer, Berlin, Heidelberg pp. 47-58, 2007.
[6] E.Duneja, A.K.Sachan, “A survey on frequent itemset mining with association rules”, International Journal of Computer Applications. 2012 May,Vol.46,No.23,pp.18-24,2012.
[7] E.Poovammal, M.Ponnavaikko,“Utility Independent Privacy Preserving Data Mining on Vertically Partitioned Data”, 1.J. Comput. Sci., Vol.5, pp.666-673,2009.
[8] F.H.AL-Zawaidah, Y.H. Jbara, A.L. Marwan, “An improved algorithm for mining association rules in large databases”, World of Computer science and information technology journal, 2011;Vol 1(7),pp.311-6,2011.
[9] G.Grahne, J. Zhu, “ Fast algorithms for frequent itemset mining using fp-trees”,IEEE transactions on knowledge and data engineering.,2005 Oct,1Vol. 7,No.10,pp.1347-62,2005.
[10] H.Toivonen,“Sampling large databases for association rules”, InVLDB 1996 Sep 3,Vol. 96, pp. 134-145,1996.
[11] H.Yun, D.Ha, B.Hwang, K.H.Ryu,“Mining association rules on significant rare data using relative support”, Journal of Systems and Software. 2003 Sep 15,Vol.67,No.3,pp.181-91,2003.
[12] J.C. Lin, W.Gan, P.Fournier-Viger, T.P.Hong,V.S.Tseng,“ Mining potential high-utility itemsets over uncertain databases”, InProceedings of the ASE BigData & SocialInformatics 2015, ACM 2015 Oct 7, pp. 25.2015.
[13] J.Jackson,“Data mining; a conceptual overview”, Communications of the Association for Information Systems. 2002 Mar 22,Vol.8,No. 1,pp.19,2002.
[14] J.Pillai, O.P.Vyas, “Overview of itemset utility mining and its applications”,International Journal of Computer Applications, 2010 Aug,Vol. 5,No.11,pp.9-13,2010.
[15] J.S.Deogun, V.V.Raghavan, A.Sarkar, H.Sever, “Data mining: Research trends, challenges, and applications. Roughs Sets and Data Mining: Analysis of Imprecise Data” ,1997,pp.9-45,1997.
[16] L.Szathmary, A.Napoli, P.Valtchev,“Towards rare itemset mining”, InTools with Artificial Intelligence, 2007,ICTAI 2007, 19th IEEE International Conference on 2007 Oct 29,IEEE Vol.1,pp. 305-312,2007.
[17] M. Al-Maolegi, B.Arkok, “An improved apriori algorithm for association rules”, arXiv preprint arXiv:1403.3948, 2014 Mar 16,2014.
[18] M.Adda, L.Wu, S.White ,Y.Feng,“ Pattern detection with rare item-set mining” arXiv preprint arXiv:1209.3089,2012.
[19] M.Adda, L.Wu, Y.Feng, “Rare itemset mining”,InMachine Learning and Applications, ICMLA 2007,IEEE,Sixth International Conference on 2007 Dec 13,pp. 73-80, 2007.
[20] P.Fournier-Viger, J.C.Lin, B.Vo, T.T. Chi, J. Zhang, H.B. Le,“ A survey of itemset mining, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,2017 Jul,Vol.7,No 4:e1207,2017.
[21] R.Agrawal, R.Srikant, “Fast algorithms for mining association rules”, InProc. 20th int. conf. very large data bases, VLDB 1994 Sep 12 .Vol. 1215, pp. 487-499,1994.
[22] R.Agrawal, T.Imieliński, A Swami., “Mining association rules between sets of items in large databases”, InAcm sigmod record,ACM 1993 Jun 1.Vol. 22, No. 2, pp. 207-216,1993.
[23] S.A Abaya, “Association rule mining based on Apriori algorithm in minimizing candidate generation” International Journal of Scientific & Engineering Research, Vol.3,No.7,pp.1-4,2012.
[24] S.Pramod,O.P.Vyas,“Survey on frequent item set mining algorithms”,International journal of computer applications. 2015,Vol.1,2015.
[25] S.Rao,P.Gupta,“Implementing Improved Algorithm Over APRIORI Data Mining Association Rule Algorithm”, International Journal of Computer Science And Technology,Mar. 2012,pp. 489-493,2012.
[26] S.S.Thakur, S.Z.Ninoria,“An Improved Progressive Sampling based Approach for Association Rule Mining”, International Journal of Computer Applications,2017,Vol.165,No.7,2017.
[27] S.Z.Ninoria, S.S.Thakur, “Study of High Utility Itemset Mining”, International Journal of Computer Applications. October 2017, Vol.175,No.4,pp.43-50,2017.
[28] Savasere A, Omiecinski ER, Navathe SB. “An efficient algorithm for mining association rules in large database”. Georgia Institute of Technology; 1995.(502).(1995)
[29] V.T.Chakaravarthy, V. Pandit, Y.Sabharwal,“Analysis of sampling techniques for association rule mining”, InProceedings of the 12th international conference on database theory 2009 ,ACM,Mar 23,pp. 276-283,2009.
[30] W.Y.Lin, M.C.Tseng, “Automated support specification for efficient mining of interesting association rules”, Journal of Information Science. 2006 Jun,Vol.32,No.3,pp.238-50,2006.
[31] Y.S.Koh, N.Rountree,“Finding sporadic rules using apriori-inverse”, InPacific-Asia Conference on Knowledge Discovery and Data Mining 2005 May 18, Springer, Berlin, Heidelberg. pp.97-106,2005.
[32] Y.Tong, X.Zhang, L.Chen,“Tracking frequent items over distributed probabilistic data”,World Wide Web, 2016 Jul 1,Vol.19,No.4,pp.579-604,2016.