CFP last date
20 June 2024
Reseach Article

Efficient Classifier Generation over Stream Sliding Window using Associative Classification Approach

by K. Prasanna Lakshmi, C.r.k.reddy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 22
Year of Publication: 2015
Authors: K. Prasanna Lakshmi, C.r.k.reddy

K. Prasanna Lakshmi, C.r.k.reddy . Efficient Classifier Generation over Stream Sliding Window using Associative Classification Approach. International Journal of Computer Applications. 115, 22 ( April 2015), 1-9. DOI=10.5120/20280-1123

@article{ 10.5120/20280-1123,
author = { K. Prasanna Lakshmi, C.r.k.reddy },
title = { Efficient Classifier Generation over Stream Sliding Window using Associative Classification Approach },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 22 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { },
doi = { 10.5120/20280-1123 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:55:32.444882+05:30
%A K. Prasanna Lakshmi
%A C.r.k.reddy
%T Efficient Classifier Generation over Stream Sliding Window using Associative Classification Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 22
%P 1-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Prominence of data streams has dragged the interest of many researchers in the recent past. Mining associative rules generated on data streams for prediction has raised greater research interest in recent years. Associative classification mining has shown better performance over many former classification techniques in Data Mining and Data Stream Mining domains. This paper introduces a new technique for mining data streams using associative classification. To the best of our knowledge there are only few techniques existing. We designed a compact data structure to efficiently maintain data streams without losing any important information. We present a PSToSW for mining rules from the tree. Subsequently, an optimized algorithm called PSToSWMine is proposed for mining a classifier which contains set of high qualified classification rules. We then conduct experiments using synthetic and real data sets to assess the performance of our approach. The experimental results show that our technique is superior to existing algorithms which perform similar tasks in terms of accuracy of prediction and run time efficiency.

  1. B. Liu, W. Hsu, and Y. Ma, "Integrating Classification and Association Rule Mining," Proc. Fourth Int'l Conf. Knowledge Discovery and Data Mining (KDD '98), Aug. 1998.
  2. C. K. S. Leung, Q. I. Khan, DSTree: a tree structure for the mining of frequent sets from data streams, in: Proc. ICDM, 2006, pp. 928– 932.
  3. Chuancong Gao, Jianyong Wang, "Efficient item set generator discovery over a stream sliding window" in C IKM'09, November 2009, Hong Kong, China, ACM 978-1-60558-512-3/09/11
  4. Hong Yao, H. J Hamilton (2006)," Mining item set utilities from transaction data bases", IEEE Transactions on Data and Knowledge Engineering, volume 59, issue 3, pp. 603-626.
  5. H. Cheng, X. Yan, J. Han, and P. S. Yu. Direct discriminative pattern mining for effective classification. In Proceedings of the 24th International Conference on Data Engineering, pages 169–178, Cancun, Mexico, 2008. IEEE.
  6. J. H. Chang, W. S. Lee, estWin: Online data stream mining of recent frequent item sets by sliding window method, Journal of Information Science 31 (2) (2005) 76–90.
  7. J. Li, D. Maier, K. Tuftel, V. Papadimos, P. A. Tucker, No pane, no gain: efficient evaluation of sliding-window aggregates over data streams, SIGMOD Record 34 (1) (2005) 39–44.
  8. J. Wang and G. Karypis. On mining instance-centric classification rules. IEEE Trans. Knowledge Data Engineering 18(11):1497–1511, 2006
  9. Koh, and Shieh, 2004. An efficient approach for maintaining association rules based on adjusting FP-tree structures. In Proc. of DASFAA 2004. Springer-Verlag, Berlin Heidelberg New York, 417–424.
  10. K. Prasanna Lakshmi, Dr. C. R. K. Reddy, "Compact Tree for Associative Classification of Data Stream Mining", IJCSI International Journal of Computer Science Issues, Vol 9, Issue 2, No 2, March 2012, ISSN(online) : 1694-0814
  11. K. Prasanna Lakshmi, Dr. C. R. K. Reddy, "A Survey on Different Trends in Data Streams " pp. 451-455, In Proc of 2010 IEEE International Conference on Networking and Information Technology, (ICNIT'10), 2010. ISBN : 978-1-4244-7577-3.
  12. L. Su, H. Liu and Z. Song, "A New Classification Algorithm for data stream". I. J. Modern Education and Computer Science, 4, 32-39, 2011.
  13. W. Li, J. Han, and J. Pei, "CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules," Proc. IEEE Int'l Conf. Data Mining (ICDM '01), Nov. 2001.
  14. R. C. Agarwal, C. C. Agarwal, and V. V. V. Prasad. A tree projection algorithm for generation of frequent item sets. Journal of Parallel and Distributed Computing, 61(3):350– 371, 2001.
  15. Snedecor. W, and Cochran. W(1989) Statistical Methods, Eighth Edition, Iowa State University Press.
  16. Tanbeer, S. K. , Ahmed, C. F. , Jeong, B. -S. , and Lee, 2008. CP-tree: a tree structure for single-pass frequent pattern mining. In Proc. of PAKDD, Lect Notes Artif Int, 1022-1027.
Index Terms

Computer Science
Information Sciences


Data Streams Sliding window Associative Classification Frequent item sets.