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High Utility Pattern Mining � A Deep Review

A.A. Tale1 , N.R. Wankhade2

Section:Review Paper, Product Type: Journal Paper
Volume-4 , Issue-12 , Page no. 120-124, Dec-2016

Online published on Jan 02, 2016

Copyright © A.A. Tale, N.R. Wankhade . 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: A.A. Tale, N.R. Wankhade, “High Utility Pattern Mining � A Deep Review,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.120-124, 2016.

MLA Style Citation: A.A. Tale, N.R. Wankhade "High Utility Pattern Mining � A Deep Review." International Journal of Computer Sciences and Engineering 4.12 (2016): 120-124.

APA Style Citation: A.A. Tale, N.R. Wankhade, (2016). High Utility Pattern Mining � A Deep Review. International Journal of Computer Sciences and Engineering, 4(12), 120-124.

BibTex Style Citation:
@article{Tale_2016,
author = {A.A. Tale, N.R. Wankhade},
title = {High Utility Pattern Mining � A Deep Review},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {12 2016},
volume = {4},
Issue = {12},
month = {12},
year = {2016},
issn = {2347-2693},
pages = {120-124},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1144},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1144
TI - High Utility Pattern Mining � A Deep Review
T2 - International Journal of Computer Sciences and Engineering
AU - A.A. Tale, N.R. Wankhade
PY - 2016
DA - 2017/01/02
PB - IJCSE, Indore, INDIA
SP - 120-124
IS - 12
VL - 4
SN - 2347-2693
ER -

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Abstract

The mining high utility pattern is new development in area of data mining. Problem of mining utility pattern with itemset share framework is tricky one as no anti-monotonicity property with interesting measure. Former works on this problem employ a two-phase, candidate generation approach with one exception that is however inefficient and not scalable with large database. This paper reviews former implementation and strategies to mine out high utility pattern in details. We will look ahead some strategies of mining sequential pattern.

Key-Words / Index Term

Data Mining, Pattern Mining, High Pattern, Frequent Pattern, Utility Mining

References

[1] L. Geng and H. J. Hamilton, �Interestingness measure for data mining: A survey,� ACM Comput. Survey, Volume-38, No. 3, Page No 9, 2006
[2] A. Silberschatz and A. Tuzhilin, �On subjective measures of interestingness in knowledge discovery,� in Proc. ACM 1st Int. Conf. Knowl. Discovery Data Mining, Page No (275-281), 1995
[3] H. Yao, H. J. Hamilton, and L. Geng, �A unified framework for utility-based measures for mining itemsets,� in Proc. ACM SIGKDD 2nd Workshop Utility-Based Data Mining, pp (28�37), 2006.
[4] R. Agrawal, T. Imielinski, and A. Swami, �Mining association rules between sets of items in large databases,� in Proc. ACM SIGMOD Int. Conf. Manage. Data, pp. (207�216), 1993.
[5] R. Agrawal and R. Srikant, �Fast algorithms for mining association rules,� in Proc. 20th Int. Conf. Very Large Databases, pp. (487�499), 1994.
[6] J. Han, J. Pei, and Y. Yin, �Mining frequent patterns without candidate generation,� in Proc. ACM SIGMOD Int. Conf. Manage. Data, pp. (1�12), 2000.
[7] M. J. Zaki, �Scalable algorithms for association mining,� IEEE Trans. Knowl. Data Eng., Volume. 12, no. 3, pp. (372�390), May/Jun. 2000.
[8] H. Yao, H. J. Hamilton, and L. Geng, �A unified framework for utility-based measures for mining itemsets,� in Proc. ACM SIGKDD 2nd Workshop Utility-Based Data Mining, pp. (28�37), 2006.
[9] R. J. Hilderman, C. L. Carter, H. J. Hamilton, and N. Cercone, �Mining market basket data using share measures and characterized itemsets,� in Proc. PAKDD, pp. (72�86), 1998.
[10] H. Yao and H. J. Hamilton, �Mining itemset utilities from transaction databases,� Data Knowl. Eng., Volume- 59, No- 3, Page No (603�626), 2006.
[11] H. Yao, H. J. Hamilton, and C. J. Butz, �A foundational approach to mining itemset utilities from databases,� in Proc. SIAM Int. Conf. Data Mining, pp. (482�486), 2004.
[12] C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, and Y.-K. Lee, �Efficient tree structures for high utility pattern mining in incremental databases,� IEEE Trans. Knowl. Data Eng., Volume-21, No-12, Page No � (1708� 1721), Dec. 2009.
[13] A. Erwin, R. P. Gopalan, and N. R. Achuthan, �Efficient mining of high utility itemsets from large datasets,� in Proc. 12th Pacific-Asia Conf. Adv. Knowl. Discovery Data Mining, Page No- (554�561), 2008.
[14] Y.-C. Li, J.-S. Yeh, and C.-C. Chang, �Isolated items discarding strategy for discovering high utility itemsets,� Data Knowl. Eng., Volume-64, No. 1, Page No. (198�217), 2008.
[15] Y. Liu, W. Liao, and A. Choudhary, �A fast high utility itemsets mining algorithm,� in Proc. Utility-Based Data Mining Workshop SIGKDD, Page No. (253�262), 2005.
[16] V. S. Tseng, B.-E. Shie, C.-W. Wu, and P. S. Yu, �Efficient algorithms for mining high utility itemsets from transactional databases,� IEEE Trans. Knowl. Data Eng., Volume- 25, No- 8, Page No � (1772�1786), Aug. 2013.
[17] J. Han, J. Pei, and Y. Yin, �Mining frequent patterns without candidate generation,� in Proc. ACM SIGMOD Int. Conf. Manage. Data, Page No- (1�12), 2000.
[18] M. J. Zaki, �Scalable algorithms for association mining,� IEEE Trans. Knowl. Data Eng., Volume - 12, No- 3, Page No � (372�390), May/Jun. 2000.
[19] J. Pei, J. Han, and V. Lakshmanan, �Pushing convertible constraints in frequent itemset mining,� Data Mining Knowl. Discovery, Volume - 8, No - 3, Page No � (227�252), 2004.
[20] C. Bucila, J. Gehrke, D. Kifer, and W. M. White, �Dualminer: A dual-pruning algorithm for itemsets with constraints,� Data Mining Knowl. Discovery, Volume - 7, No - 3, Page No � (241�272), 2003.
[21] F. Bonchi, F. Giannotti, A. Mazzanti, and D. Pedreschi, �ExAnte: A preprocessing method for frequent-pattern mining,� IEEE Intell. Syst., Volume - 20, No - 3, Page No � (25�31), May/Jun. 2005.
[22] L. De Raedt, T. Guns, and S. Nijssen, �Constraint programming for itemset mining,� in Proc. ACM SIGKDD, Page No � (204�212), 2008.
[23] R. Bayardo and R. Agrawal, �Mining the most interesting rules,� in Proc. 5th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Page No- (145�154), 1999.
[24] S. Morishita and J. Sese, �Traversing itemset lattice with statistical metric pruning,� in Proc. 19th ACM Symp. Principles Database Syst., Page No - (226�236), 2000.
[25] R. J. Hilderman and H. J. Hamilton, �Measuring the interestingness of discovered knowledge: A principled approach,� Intell. Data Anal., Volume -7, No- 4, Page No- (347�382), 2003.
[26] P. N. Tan, V. Kumar, and J. Srivastava,, �Selecting the right objective measure for association analysis,� Inf. Syst., Volume - 29, No - 4, Page No � (293�313), 2004.
[27] R. J. Hilderman, C. L. Carter, H. J. Hamilton, and N. Cercone, �Mining market basket data using share measures and characterized itemsets,� in Proc. PAKDD, Page No -(72�86), 1998.
[28] H. Yao, H. J. Hamilton, and C. J. Butz, �A foundational approach to mining itemset utilities from databases,� in Proc. SIAM Int. Conf. Data Mining, Page No � (482�486), 2004.
[29] C. H. Cai, A. W. C. Fu, C. H. Cheng, and W. W. Kwong, �Mining association rules with weighted items,� in Proc. Int. Database Eng. Appl. Symp., Page No � (68�77), 1998.
[30] T. Y. Lin, Y. Y. Yao, and E. Louie, �Value added association rules,� in Proc. 6th Pacific-Asia Conf. Adv. Knowl. Discovery Data Mining, Page No - (328�333), 2002.
[31] S. Lu, H. Hu, and F. Li, �Mining weighted association rules,� Intell. Data Anal., Volume- 5, No - 3, Page No � (211�225), 2001.
[32] Y. Shen, Q. Yang, and Z. Zhang, �Objective-oriented utility-based association mining,� in Proc. IEEE Int. Conf. Data Mining, Page No � (426�433), 2002.
[33] R. Chan, Q. Yang, and Y. Shen, �Mining high utility itemsets,� in Proc. Int. Conf. Data Mining, Page No �(19�26), 2003.
[34] G.-C. Lan, T.-P. Hong, and V. S. Tseng, �An efficient projectionbased indexing approach for mining high utility itemsets,� Knowl. Inf. Syst., Volume - 38, No - 1, Page No � (85�107), 2014.
[35] U. Yun, H. Ryang, and K. H. Ryu, �High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates,� Expert Syst. Appl., Volume - 41, No - 8, Page No � (3861�3878), 2014.
[36] S. Dawar and V. Goyal, �UP-Hist tree: An efficient data structure for mining high utility patterns from transaction databases,� in Proc. 19th Int. Database Eng. Appl. Symp, Page No � (56�61), 2015.
[37] M. Liu and J. Qu, �Mining high utility itemsets without candidate generation,� in Proc. ACM Conf. Inf. Knowl. Manage. ,Page No � (55�64) , 2012.
[38] J. Liu, Ke Wang, and C. M. Fung, �Mining High Utility Pattern in One Phase without Generating Candidate,� IEEE Transaction on Knowledge and Data Engineering, Volume - 28, No � 5, Page No � (1245�1257), May- 2016.
[39] Jun-Zhe. Wang and Juin-Long Huang, �Itemset mining of High Utility Sequential Pattern in Incremental Database,� ACM. CIKM�16, Indianapolis, IN, USA, pp � (1245�1257), October 24th- 28th, 2016.
[40] M. Zihayat, Z. Zhenhua Hu, A. An and Yonggang Hu �Distributedand Parallel High Utility Sequential Pattern Mining,� Technical Report EECS � 2016-03, April 12th, 2016
[41] Jiaqi Ge and Yuni Xia, �Distributed Sequential Pattern Mining in Large Scale Uncertain Databases,� PAKDD Springer International Publishing Switzerland, pp � (17 -29) , 2016