CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset

by Meenu Dave, Hitesh Maharwal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 99 - Number 6
Year of Publication: 2014
Authors: Meenu Dave, Hitesh Maharwal
10.5120/17377-7913

Meenu Dave, Hitesh Maharwal . Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset. International Journal of Computer Applications. 99, 6 ( August 2014), 20-23. DOI=10.5120/17377-7913

@article{ 10.5120/17377-7913,
author = { Meenu Dave, Hitesh Maharwal },
title = { Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 99 },
number = { 6 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 20-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume99/number6/17377-7913/ },
doi = { 10.5120/17377-7913 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:27:29.536455+05:30
%A Meenu Dave
%A Hitesh Maharwal
%T Frequent Pattern Mining based on Multiple Minimum Support using Uncertain Dataset
%J International Journal of Computer Applications
%@ 0975-8887
%V 99
%N 6
%P 20-23
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association rule mining plays a major role in decision making in the production and sales business area. It uses minimum support (minsup) and support confidence (supconf) as a base to generate the frequent patterns and strong association rules. Setting a single value of minsup for a transaction set doesn't seem feasible for some real life applications. Similarly the probabilistic value of items in the transaction set may be acceptable. So generating the frequent pattern from the uncertain dataset becomes a concern factor. This research work details the aforesaid problem and proposes a solution for the same.

References
  1. Laszlo Szathmary, Amedeo Napoli, Petko Valtchev: Towards Rare Itemset Mining. ICTAI (1) 2007: 305-312
  2. Rakesh Agrawal, Tomasz lmielinski, and Arun Swami. Mining association rules between sets of items in large databases. In Proc. Of the ACM SIGMOD Conference on Management of Data, pages 207-216, Washington, D. C. , May 1993.
  3. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In Advances in knowledge discovery and data mining, pages 307–328. AAAI, 1996.
  4. R. U. Kiran and P. K. Reddy. An improved frequent pattern-growth approach to discover rare association rules. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pages 43–52, 2009.
  5. Chun-Kit Chui, Ben Kao, and Edward Hung(2007), Mining Frequent Itemsets from Uncertain Data, PAKDD 2007, LNAI 4426, pp. 47–58, 2007. © Springer-Verlag Berlin Heidelberg 2007, pp 47-58
  6. G. M. Weiss. Mining with rarity: a unifying framework. SIGKDD Explor. Newsl. , 6(1):7–19, 2004.
  7. B. Liu, W. Hsu, and Y. Ma. Mining association rules with multiple minimum supports. In KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 337–341. ACM, 1999.
  8. J. Han, J. Pei, Y. Yin, and R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. , 8(1):53–87, 2004.
  9. Y. -H. Hu and Y. -L. Chen. Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism. Decis. Support Syst. , 42(1):1–24, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Mining Minimum Support (minsup) Support Confidence (supconf) Uncertain Dataset

Powered by PhDFocusTM