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Reseach Article

A Novel Acceleration Technique to Improve the Speed of Mining Frequent U2 Patterns

Published on January 2013 by K. S. Kalaivani, S. Kuppuswami
Amrita International Conference of Women in Computing - 2013
Foundation of Computer Science USA
AICWIC - Number 4
January 2013
Authors: K. S. Kalaivani, S. Kuppuswami
41ebf8a5-5972-4e1c-9779-58fa26e17f75

K. S. Kalaivani, S. Kuppuswami . A Novel Acceleration Technique to Improve the Speed of Mining Frequent U2 Patterns. Amrita International Conference of Women in Computing - 2013. AICWIC, 4 (January 2013), 5-9.

@article{
author = { K. S. Kalaivani, S. Kuppuswami },
title = { A Novel Acceleration Technique to Improve the Speed of Mining Frequent U2 Patterns },
journal = { Amrita International Conference of Women in Computing - 2013 },
issue_date = { January 2013 },
volume = { AICWIC },
number = { 4 },
month = { January },
year = { 2013 },
issn = 0975-8887,
pages = { 5-9 },
numpages = 5,
url = { /proceedings/aicwic/number4/9881-1323/ },
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 K. S. Kalaivani
%A S. Kuppuswami
%T A Novel Acceleration Technique to Improve the Speed of Mining Frequent U2 Patterns
%J Amrita International Conference of Women in Computing - 2013
%@ 0975-8887
%V AICWIC
%N 4
%P 5-9
%D 2013
%I International Journal of Computer Applications
Abstract

Frequent pattern mining is the method of finding patterns like itemsets, subsequences and substructures that repeatedly occur in a dataset. In Univariate Uncertain data, each attribute present in a transaction is represented by a quantitative interval and a probability value. U2P-Miner algorithm is used to mine frequent patterns from U2 data. The number of intervals has a great impact on the time taken for mining frequent patterns. A novel acceleration technique which compares the expected support with the user specified threshold is introduced to minimize the number of intervals thereby improving the speed of the mining process. The runtime of the modified U2P-Miner algorithm is compared with the existing U2P-Miner algorithm.

References
  1. Agrawal, R. , & Srikant, R. 1994, September. Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, VLDB (Vol. 1215, pp. 487-499).
  2. Han, J. , Pei, J. , & Yin, Y. 2000, May. Mining frequent patterns without candidate generation. In ACM SIGMOD Record (Vol. 29, No. 2, pp. 1-12). ACM.
  3. Pei, J. , Han, J. , Lu, H. , Nishio, S. , Tang, S. , & Yang, D. 2001. H-mine: Hyper-structure mining of frequent patterns in large databases. In Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on (pp. 441-448). IEEE.
  4. Liu, Y. H. 2012. Mining frequent patterns from univariate uncertain data. Data & Knowledge Engineering, 71(1), 47-68.
  5. Chui, C. K. , Kao, B. , & Hung, E. 2007. Mining frequent itemsets from uncertain data. Advances in Knowledge Discovery and Data Mining, 47-58.
  6. Leung, C. S. , Carmichael, C. L. , & Hao, B. 2007, October. Efficient mining of frequent patterns from uncertain data. In Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on (pp. 489-494). IEEE.
  7. Leung, C. K. S. , & Brajczuk, D. A. 2009, June. Efficient algorithms for mining constrained frequent patterns from uncertain data. In Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data (pp. 9-18). ACM.
  8. Aggarwal, C. C. , Li, Y. , Wang, J. , & Wang, J. 2009, June. Frequent pattern mining with uncertain data. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 29-38). ACM.
  9. Zimányi, E. , & Pirotte, A. 1996. Imperfect information in relational databases. Uncertainty Management in Information Systems, 35-88.
  10. Bernecker, T. , Kriegel, H. P. , Renz, M. , Verhein, F. , & Zuefle, A. 2009, June. Probabilistic frequent itemset mining in uncertain databases. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 119-128). ACM.
  11. Srikant, R. , & Agrawal, R. 1996, June. Mining quantitative association rules in large relational tables. In ACM SIGMOD Record (Vol. 25, No. 2, pp. 1-12). ACM.
  12. http://taqm. epa. gov. tw/taqm/zh-tw/default. aspx.
Index Terms

Computer Science
Information Sciences

Keywords

U2p-tree Univariate Uncertain Data Modified U2p-miner