CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

MISP (Modified IncSpan+): Incremental Mining of Sequential Patterns

by Anil Kumar, Vijender Kumar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 8
Year of Publication: 2013
Authors: Anil Kumar, Vijender Kumar
10.5120/10945-5903

Anil Kumar, Vijender Kumar . MISP (Modified IncSpan+): Incremental Mining of Sequential Patterns. International Journal of Computer Applications. 65, 8 ( March 2013), 23-31. DOI=10.5120/10945-5903

@article{ 10.5120/10945-5903,
author = { Anil Kumar, Vijender Kumar },
title = { MISP (Modified IncSpan+): Incremental Mining of Sequential Patterns },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 8 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number8/10945-5903/ },
doi = { 10.5120/10945-5903 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:18:19.217828+05:30
%A Anil Kumar
%A Vijender Kumar
%T MISP (Modified IncSpan+): Incremental Mining of Sequential Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 8
%P 23-31
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Real life sequential databases are usually not static. They grow incrementally. So after every update a frequent pattern may no longer remains frequent while some infrequent patterns may appear as frequent in updated database. It is not a good idea to mine sequential database from scratch every time as the update occurs. It would be better if one can use the knowledge of already mined sequential patterns to find the complete set of sequential patterns for updated database. An incremental mining algorithm does the same thing. The main goal of an incremental mining algorithm is to reduce the time taken to find out the frequent patterns significantly i. e. it should mine the set of frequent patterns in significantly less time than a non-incremental mining algorithm. In this work the efficiency has been improved, in time and space, of an already existing incremental mining algorithm called IncSpan+ which is claimed to rectify an incremental mining algorithm called IncSpan.

References
  1. Nguyen S. N. , Sun X. , Orlowska M. E. , "Improvements of IncSpan: Incremental Mining of Sequential Patterns in Large Database", Proc. of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2005.
  2. Cheng H. ,YanX. ,Han J. , "IncSpan: Incremental Mining of sequential Patterns in large database. Proc. ACM KDD Conf. on Knowledge Discovery in Data, Washington (KDD'04). 2004.
  3. Agrawal R. , Srikant R. : Mining sequential patterns. Proc. 11th IEEE Int. Conf. on Data Engineering (ICDE'95),1995.
  4. Pei J. , Han J. , Mortazavi-Asl B. , Wang J. , Pinto H. , Chen Q. , Dayal U. , Hsu M. : Mining sequential patterns by Pattern-Growth: The PrefixSpan approach. IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 10, 2004.
  5. Srikant R. , Agrawal R. : Mining sequential patterns: Generalizations and performance improvements. Proc. 5th IEEE Int. Conf. on Extending Database Technology (IDBT'96).
  6. Zaki M. : SPADE: An efficient algorithm for mining frequent sequences,Machine Learning, 40: (31-60), 2001
  7. Parthasarathy S. , Zaki M. , Ogihara M. , and Dwarkadas S. : Incremental and interactive sequence mining. Proc. 8th Int. Conf. on Information and Knowledge Management (CIKM'99), 1999.
  8. Zhang M. , Kao B. , Cheung D. , Yip C. : Efficient algorithms for incremental update of frequent sequences. Proc. of Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'02), 2002.
  9. X. Yan, J. Han, and R. Afshar. Clospan: Mining closed sequential patterns in large datasets, 2003.
  10. J. Han, J. Pei, B. Mortazavi-Asl,Q. Chen, U. Dayal, and M. C Hsu, "FreeSpan: Frequent Pattern-Projected Sequential Pattern Min- ing,"Proc. 2000 ACM SIGKDD Int'l Conf. Knowledge Discovery in Databases (KDD '00),pp. 355-359,Aug. 2000.
  11. I. H. Witten, and F. Frank,Data Mining: Practical Machine Learning Tools With Java Implementations. ,San Francisco, CA: Morgan Kauf- man,2000.
  12. Dao-I Lin, and Zvi M. Kedem, "Pincer Search: An Efficient Algorithm For Discovering The Maximum Frequent Set," IEEE Trans. on Knowledge and Data Engineering, Vol. 14, No. 3, (May/June, 2002), pp. 553-566.
  13. F. Masseglia, P. Poncelet, and M. Teisseire. Incremental mining of sequential patterns in large databases. Data Knowl. Eng. , 46:97–121, 2003.
  14. F. Masseglia, P. Poncelet, M. Teisseire, "Incremental mining of sequential patterns in large databases," Actes des Jouenes Bases de DonnesAvances (BDA'00), Blois, France, 1999.
  15. J. Ayres, J. E. Gehrke, T. Yiu and J. Flannick, " Sequential pattern mining using bitmaps. ", Proc. 2002 ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD'02), July 2002.
  16. Jiawei Han and MichelineKamber, "Data Mining: Concepts and Techniques", 2nd edition, Morgan Kaufmann Published, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Set of frequent sequential patterns (FP) Boundary of semi-frequent sequential patterns (BSFP)

Powered by PhDFocusTM