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Reseach Article

Mining Positive and Negative Sequential Pattern in Incremental Transaction Databases

by Vinay Kumar Khare, Vedant Rastogi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 71 - Number 1
Year of Publication: 2013
Authors: Vinay Kumar Khare, Vedant Rastogi
10.5120/12322-8539

Vinay Kumar Khare, Vedant Rastogi . Mining Positive and Negative Sequential Pattern in Incremental Transaction Databases. International Journal of Computer Applications. 71, 1 ( June 2013), 18-22. DOI=10.5120/12322-8539

@article{ 10.5120/12322-8539,
author = { Vinay Kumar Khare, Vedant Rastogi },
title = { Mining Positive and Negative Sequential Pattern in Incremental Transaction Databases },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 71 },
number = { 1 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume71/number1/12322-8539/ },
doi = { 10.5120/12322-8539 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:34:20.716494+05:30
%A Vinay Kumar Khare
%A Vedant Rastogi
%T Mining Positive and Negative Sequential Pattern in Incremental Transaction Databases
%J International Journal of Computer Applications
%@ 0975-8887
%V 71
%N 1
%P 18-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Positive and negative sequential patterns mining is used to discover interesting sequential patterns in a incremental transaction databases, and it is one of the essential data mining tasks widely used in various application fields. Implementation of this approach, construct tree for appended transactions (new upcoming data) and will merge this tree with existing tree (tree of existing transactions) to get the Updated tree. Positive and negative sequential Patterns mining is an aim to find more interesting sequential patterns, considering the minimum support of each data item in a sequence database. Generally, the generation order of data elements is considered to find sequential patterns. Positive sequential patterns states that these items were occur with one and another. Actually the absence of certain itemset may imply the appearance of other itemsets as well. The absence of itemsets thus is becoming measurable in many applications. Negative sequential patterns could assist product recommendation systems to make more accurate decisions. This approach will reduce the mining time for incremental database if the Existing database has lots of transactions and Appended database having few transactions.

References
  1. Weimin Ouyang, Qinhua Huang,"Mining Positive and Negative Sequential Patterns with Multiple Minimum Supports in Large Transaction Databases", IEEE Second WRI Global Congress on Intelligent Systems 2010.
  2. Sue-Chen Hsueh1, Ming-Yen Lin2, Chien-Liang Chen2, "Mining Negative Sequential Patterns for E-Commerce Recommendations", IEEE, Asia-Pacific Services Computing Conference, 2008.
  3. R. Uday Kiran and P. Krishna Reddy," An Improved Multiple Minimum Support Based Approach to Mine Rare Association Rules", Computational Intelligence and Data Mining, CIDM '09, IEEE Symposium on 2009.
  4. William Cheung and Osmar R. Zaiane - Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint, University of Alberta, Edmonton, Canada { wcheung, zaiane} @cs. ualberta. ca 2003.
  5. Yun, H. , Ha, D. , Hwang, B. , and Ryu K. H. "Mining association rules on significant rare data using relative support. ", The Journal of Systems and Software 67, 2003, pp. 181-191.
  6. Savasere A, Omiecinski E and Navathe S. "Mining for Strong Negative Associations in a Large Database of Customer Transactions" In Proc. 1998 Int. Conf. on Data Engineering, pp. 494-502.
  7. Luis, R. , Redol, J. , Simoes, D. , Horta, N. , "Data Warehousing and Data Mining System Applied to ELearning, Proceedings of the II International Conference on Multimedia and Information & Communication Technologies in Education, Badajoz, Spain, December 3-6th 2003.
  8. Agrawal, R. , Imielinski, T. , and Swami, A. N. 1993. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207-216.
  9. Jiawai Han, Jian Pai, Yiwen Yin, Runying Mao-Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach © Kluwer Academic Publishers 2004.
  10. Fayyad, U. , Piatetsky-Shapiro, G. , and Smyth, R (1996). "The KDD Process for Extracting Useful Knowledge from Volumes of Data," Communications of the ACM, (39:11), pp. 27-34.
  11. Han, J. , Kamber, M. (2001), Data Mining: Concepts and Techniques, Morgan-Kaufmann Academic Press, San Francisco.
  12. Hand, D. J. (1998), "Data Mining: Statistics and More?", The American Statistician, May (52:2), 112-118.
  13. Han, J. , Kamber, M. (2001), Data Mining: Concepts and Techniques, Morgan-Kaufmann Academic Press, San Francisco.
  14. Fayyad, U. M. , Piatetsky-Shapiro, G. , Smyth, P. , Uthurusamy, R, Editors (1996), Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, MA.
  15. Rajagopalan, B. , Krovi, R. (2002), "Benchmarking Data Mining Algorithms", Journal of Database Management, Jan-Mar, 13, 25-36.
Index Terms

Computer Science
Information Sciences

Keywords

Appended Database Existing Itemsets Negative Positive Patterns Sequential