Use of Constraints in Pattern Mining: A Survey
Keywords:
constraint, frequent, sequence, pattern, miningAbstract
Constraint based pattern mining and association rules are used in many applications like genetic sequence analysis, in finance for bankrupting prediction, in securities for fraud detection, in agriculture for discovering classification of plants etc. to get the user interesting knowledge. Constraints are useful to eliminate unwanted rules and also solves rule explosion problem. Many algorithms are proposed for constraint based pattern mining and association rule generation. These constraints are in the form of attribute, item length, time or duration, regular expression etc. Pushing constraints in a mining process gives user interesting discovery. Literature survey shows that performance of an algorithm improves with application of constraint during the mining process. The paper elaborates about the literature survey on use of constraints in generation of association rules with different categories of constraints with its properties.
References
Ramakrishnan Srikant and Rakesh Agrawal,”Mining Sequential Patterns: Generalizations and Performance Improvements‖”, Advances in Database Technology — EDBT ‘96 Lecture Notes in Computer Science, Springer, Volume 1057, pp 1-17, 1996
Ayres, J.,Gehrke, J.Yiu T. and Flannick J.’ Sequential Pattern Mining using Bitmap representation in proceeding of ACM SIGKDD’02,pp 429-35,2002
M.J.Zaki, “Spade: An efficient algorithm for mining frequent sequences”, Machine Learning 42 (2001), pp. 31-60
J.Pei, J han, B.Mortazavi PrefixSpan: Mining Sequential Patterns efficiently by prefix projected pattern growth ,ICDE 2001,Heidelberg,Germany,2001 pg 215-224
H.Han, J.Pei, B.Mortazavi- Asl,Q.Chen, U. Dayal and M-C Hsu(2000) FreeSpan: Frequent Pattern projected Sequential Pattern mining: Proceedings of 2000 International Conference on Knowledge discovery and data mining pp 355-359
Jiawei Han,Micheline Kamber,Jain Pei, “Data Mining Concepts and Techniques”,Third Edition,2012,ISBN:978-0-12-381479-1.
J.Pei, J.han and W.Wang, “Constraint based sequential pattern mining: the pattern growth method”, in journal of Intelligent Information systems (2007), pp.133-160.
M.N.Garofalakis, R.Rastogi and K.Shim, “ Spirit: Sequential pattern mining with regular expression constraints”, In Atkinson, M.P., Orlowska, M.E., Valduriez , P., Zdonik, S.B., Brodie, M.L., eds.: VLDB’99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK, Morgan Kaufmann (1999),pp. 223-234 .
Shigeaki Sakurai, Youichi Kitahara and Ryohei Orihana, “Sequential Pattern Mining based on new criteria and attribute constraint”,IEEE International Conference on Systems,Man and Cybernetics 2007.
Marion Leleu,Christophe Rigotti,Jean-Francois Boulicar and guillaume Euvrard, “Constraint Based Mining of Sequential Patterns over Dataset with Consecutive Repetitions”,PKDD 2003.
Yen-Liang Chen,Ya-Han Hu, “Constraint Based Sequential Pattern Mining:The Consideration of Recency and Compactness ”,Decision Support System by Elseveir 42(2006) pp.1203-1215.
Tedeusz Morzy,Marek Wajeiechowski,Maciej Zakrzewicz,” Efficient Constraint based Sequential Pattern Mining using dataset filtering techniques”, Proceedings of the Baltic Conference, BalticDB&IS 2002, Volume 1,2002
Yu Hirate, H.Yamana, “Generalized sequential pattern mining with item interval”,Proceedings of 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining 2006.
L.Gomez, A.A.Vaisman, “Re-SPAM: Using Regular Expression for sequential pattern mining in Trajectory database”, IEEE International Conference on Data Mining workshops 2008
D.Senthil Kumar , N.Jayaveeran, “ A survey on Association Rule Mining Algorithms for Frequent Itemset ”,International Journal of Computer Science and Engineering,Vol.4,Issue-10,Oct-2016
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