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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 4/2018

23-05-2016 | Original Article

Association rule mining using treap

Authors: H. S. Anand, S. S. Vinodchandra

Published in: International Journal of Machine Learning and Cybernetics | Issue 4/2018

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Abstract

The analytical process designed to mine data became more difficult with the rapid information explosion. This in-turn created completely distributed and un-indexed data. Thus assessing and finding relations between variables from large database became a tedious task. There are various association rule mining algorithms available for this process, but a powerful association algorithm which runs in reduced time and space complexity is hard to find. In this work, we propose a new rule mining algorithm which works in a priority model for finding interesting relations in a database using the data structure Treap. While comparing with Apriori’s O (en) and FP growth’s O (n2), the proposed algorithm finishes mining in O (n) in its best case analysis and in O (n log n) in its worst case analysis. This was found to be much better when compared to other algorithms of its kind. The results were evaluated and compared with the existing algorithm.
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Literature
1.
Suneetha KR, Krishnamoorti R (2010) Advanced version of a priori algorithm. In: Proceedings of IEEE-ICIIC, pp 238–245
2.
Pei J (2002) Pattern growth methods for frequent pattern mining. In: thesis submitted for Doctor of Philosophy, Simon Fraser University, pp 99–134
3.
Boney L, Tewfik AH, Hamdy KN (2006) Minimum association rule in large Database. In: Proceedings of Third IEEE International Conference on Computing, pp 12–16
4.
Agarwal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of VLDB, pp 487–499
5.
Bodon F (2003) A fast apriori implementation. In: Proceedings of IEEE ICDM workshop on frequent item set mining implementation, vol 9
6.
Borgelt C (2004) Recursion pruning for the apriori algorithm. In: Proceedings of 2nd IEEE ICDM workshop on frequent item set mining implementations, vol 126
7.
Zaki M, Parthasarathy S, Ogihara M, Li W (1997) New algorithms for fast discovery of association rules. In: Proceedings of 3rd international conference on knowledge discovery and data mining, vol 2, pp 283–296
8.
Anandhavalli, Gautaman K (2007) Association rule mining in genomics. Int J Comput Theory Eng, vol 1
9.
Cooper C, Zito M (2007) Realistic synthetic data for testing association rule mining algorithms for market basket databases. Knowl Discov Databases: PKDD 9:398–405
10.
Varde AS, Takahashi M, Rundensteiner EA, Ward MO, Maniruzzaman M, Sisson RD (2004) Apriori algorithm and game of life for predictive analysis in materials science. Int J Knowl Based Intell Eng Syst 8:116–122
11.
Wu H, Lu Z, Pan L, Xu R, Jiang W (2009) An improved apriori based algorithm for association rules mining. In: Proceedings of sixth international conference on fuzzy systems and knowledge discovery, pp 51–55
12.
Sun D, Teng S, Zhang W, Zhu H (2007) An algorithm to improve the effectiveness of apriori. In: Proceedings of 6th IEEE international conference on cognitive informatics, vol 1, pp 385–390
13.
Bodon F (2003) A fast apriori implementation. In: Proceedings of IEEE ICDM workshop on frequent item-set mining implementation, vol 9
14.
Kryszkiewicz M, Rybiński H (2000) Data mining in incomplete information systems from rough set perspective. Rough Set Methods Appl 56:567–580MathSciNetCrossRefMATH
15.
Kosters AW, Marchiori E, Oerlrmans AJ (1999) Mining clusters with association rules. Third symposium on intelligent data analysis. In: Proceedings of Springer Lecture Notes in Computer Science, pp 39–50
16.
Lin TY (1996) Rough set theory in very large databases. Symp Model, Anal Simul 2:936–941
17.
Borgelt C (2005) An implementation of FP growth algorithm. In: Proceedings of workshop on open source mining software ACM
18.
Malik K, Raheja N, Garg P (2011) Enhance FP growth algorithm. Int J Comput Eng Manag 12:54–57
19.
Anand HS, Vinodchandra SS (2014) Horizontal and vertical rule mining algorithms, ACCIS. In: Proceedings of Elsevier, pp 26–28
20.
Vinodchandra SS, Hareendran S (2014) Artificial intelligence and machine learning, 1st edn. PHI publishers, Delhi
21.
Guy EB, Margaret RM (1998) Fast set operations using treaps. In: Proceedings of the tenth annual ACM symposium on parallel algorithms and architectures, pp 16–26
22.
23.
Mayadevi N, Vinodchandra SS, Ushakumari S (2015) SCADA based operator support system for power plant fault diagnosis. ACIDS-2015. In: Proceedings of Springer, pp 23–26
24.
Mayadevi N, Vinodchandra SS, Ushakumari S (2014) Expert system for power plant operator performance evaluation. In: IEEE ICACC, pp 27–29
25.
Wu H, Lu Z, Pan L, Xu R, Jiang W (2009) An improved apriori based algorithm for association rules mining. In: Proceedings of sixth international conference on fuzzy systems and knowledge discovery, pp 51–55
26.
Das R, Bhattacharyya DK, Kalita JK (2010) Clustering gene expression data using an effective dissimilarity measure. Int J Comput Biosci 1:55–68CrossRef
27.
28.
Wang Xizhao, Wang Yadong, Xiaofei Xu, Ling Weide, Yeung Daniel (2001) A new approach to fuzzy rule generation: fuzzy extension matrix. Fuzzy Sets Syst 123(3):291–306MathSciNetCrossRefMATH
29.
Wang Xizhao, Dong Chunru, Fan Tiegang (2007) Training T-S norm neural networks to refine weights for fuzzy if-then rules. Neurocomputing 70(13–15):2581–2587CrossRef
30.
Wang Xizhao, Dong Chunru (2009) Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy. IEEE Trans Fuzzy Syst 17(3):556–567MathSciNetCrossRef
Metadata
Title
Association rule mining using treap
Authors
H. S. Anand
S. S. Vinodchandra
Publication date
23-05-2016
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 4/2018
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0546-7

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