CFP last date
20 December 2024
Reseach Article

Mining Closed-Regular Patterns in Incremental Transactional Databases using Vertical Data Format

Published on January 2013 by M. Sreedevi, L. S. S. Reddy
Amrita International Conference of Women in Computing - 2013
Foundation of Computer Science USA
AICWIC - Number 1
January 2013
Authors: M. Sreedevi, L. S. S. Reddy
32066f4e-2fc6-4d9c-a94a-2e5a031624e3

M. Sreedevi, L. S. S. Reddy . Mining Closed-Regular Patterns in Incremental Transactional Databases using Vertical Data Format. Amrita International Conference of Women in Computing - 2013. AICWIC, 1 (January 2013), 31-35.

@article{
author = { M. Sreedevi, L. S. S. Reddy },
title = { Mining Closed-Regular Patterns in Incremental Transactional Databases using Vertical Data Format },
journal = { Amrita International Conference of Women in Computing - 2013 },
issue_date = { January 2013 },
volume = { AICWIC },
number = { 1 },
month = { January },
year = { 2013 },
issn = 0975-8887,
pages = { 31-35 },
numpages = 5,
url = { /proceedings/aicwic/number1/9864-1306/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Amrita International Conference of Women in Computing - 2013
%A M. Sreedevi
%A L. S. S. Reddy
%T Mining Closed-Regular Patterns in Incremental Transactional Databases using Vertical Data Format
%J Amrita International Conference of Women in Computing - 2013
%@ 0975-8887
%V AICWIC
%N 1
%P 31-35
%D 2013
%I International Journal of Computer Applications
Abstract

Regular pattern mining on Incremental Databases is a novel approach in Data Mining Research. Recently closed item set mining has gained lot of consideration in mining process. In this paper we propose a new mining method called CRPMID (Closed-regular Pattern Mining on Incremental Databases) with sliding window technique using Vertical Data format. This method generates complete set of closed-regular patterns with support and regularity threshold values. Our Experimental results show that CRPMID method is efficient in both memory usage and execution time.

References
  1. Tanbeer and Ahmed. 2008. Regular pattern tree (RP-Tree) mines regular patterns from transactional databases. IECEC –Transactions Information and systems volume E91-D Issue-11, 2568-2577.
  2. Tanbeer and Ahmed. 2010 IncRT- Incremental regular pattern tree and pattern growth mining technique to find regular patterns on incremental databases. In Proceedings of International Asia pacific web conference.
  3. Vijay Kumar, G. , Sreedevi, M. , Pavan Kumar, N. ,V. ,S. 2011. Mining regular patterns on data streams using vertical data format. International Journal of Advanced Research in Computer Science. Volume 2, No. 5(Sept-Oct. 2011), 0976-5697.
  4. Chang-Hung Lee, Cheng–Ru Lin and Ming-Syan chen. 2001. Sliding–window Filtering: An efficient Algorithm for Incremental Mining. In Proceedings of the tenth international conference on Information and knowledge management. ACM.
  5. Chang and Lee. SWFI-Mining frequent item sets in online data streams with a transaction sliding windows model.
  6. Chi, Y. , Wang, H. , Yu, P. S. , Muntz, R. R. 2004. Moment: Maintaining closed frequent itemsets over a stream sliding window. In Proceedings of Fourth IEEE International Conference on Data Mining.
  7. Zaki, M. ,J. , Hsiao, C. ,J. 2002. CHARM: An Efficient Algorithm for Closed Item Set Mining. 2002. In Proceedings of SIAM International Conference on Data Mining.
  8. J. Pei, J. Han, and R. Mao. 2000. CLOSET Mining frequent closed item sets for Association Rules. In Proceedings of International Conference on Data Mining and Knowledge Discovery.
  9. Binesh Nair, Amiya Kumar and Tripathy. 2011. Accelerating Closed Frequent Item set Mining by Elimination of Null Transactions. Journal of Emerging Trends in Computing and Information Sciences. 2, 7 (July. 2011), 317-324.
  10. Pasquier, N. , Bastide, Y. , Taouil, R. , Lakhal, L. 1999. Efficient Mining of Association Rules using Closed Itemset Lattices, Journal of information Systems 24(1), 25-46.
  11. Uno, T. , Asai, T. , Uchida, Y. , Hiroki A. 2003. LCM: Enumerating Frequent Closed Item sets. In Proceedings of the IEEE ICDM Workshop of Frequent Itemset Mining Implementations (FIMI).
  12. Liu,X. , Guan J. , Hu, P. 2009. Mining frequent closed item sets from a landmark window over online data stream. Journal of computers and Mathematics with Applications. 57, 6 (2009). 927-936.
  13. Han J. , Cheng,H. , Xin, D. ,Yan. 2007. Frequent Pattern Mining: Current Status and future Directions. Journal of Data Mining and Knowledge Discovery. 15 (2007) 55-86.
  14. Stumme, G. , Taouil, R. , Bastide, Y. , Pasquier, N. , Lakhal, L. 2002. Computing iceberg concept lattices with TITANIC. Journal on Data and Knowledge Engineering. 42, 2 (2002). 189-222.
  15. Zaki, M. J. 2001. Generating Non-Redundant Association Rules. In Proceedings of ACM SIGKDD International Conference on knowledge Discovery and Data Mining.
  16. Yuan, D. , Lee, K. , Cheng, H. , Krishna, G. , Li, Z. , Ma, X. , Zhou, Y. , Han, J. 2008. CISpan: comprehensive incremental mining algorithms of closed sequential patterns for multi-versional software mining. In Proceedings of SIAM Int. Conf. Data Mining.
  17. Chen, Y. , Guo, J. , Wang, Y. , Xiong, Y. , & Zhu, Y. 2007. Incremental Mining of Sequential Patterns using Prefix Tree. Advances in Knowledge Discovery and Data Mining, 433-440.
Index Terms

Computer Science
Information Sciences

Keywords

Closed Patterns Regular Patterns Vertical Data Sliding Window Incremental Databases