Research Article
Survey on Frequent Pattern Discovery and its Approaches using: Data Mining
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 179 - Issue 43 |
| Published: May 2018 |
| Authors: W. Sarada, P. V. Kumar |
10.5120/ijca2018917050
|
W. Sarada, P. V. Kumar . Survey on Frequent Pattern Discovery and its Approaches using: Data Mining. International Journal of Computer Applications. 179, 43 (May 2018), 46-51. DOI=10.5120/ijca2018917050
@article{ 10.5120/ijca2018917050,
author = { W. Sarada,P. V. Kumar },
title = { Survey on Frequent Pattern Discovery and its Approaches using: Data Mining },
journal = { International Journal of Computer Applications },
year = { 2018 },
volume = { 179 },
number = { 43 },
pages = { 46-51 },
doi = { 10.5120/ijca2018917050 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2018
%A W. Sarada
%A P. V. Kumar
%T Survey on Frequent Pattern Discovery and its Approaches using: Data Mining%T
%J International Journal of Computer Applications
%V 179
%N 43
%P 46-51
%R 10.5120/ijca2018917050
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Apriori algorithm has been imperative algorithm in association rule mining. Main proposal of this algorithm is to find useful patterns between different set of data. It is the simplest algorithm yet having many drawbacks. Many researchers have been done for the enhancement of this algorithm. This paper does a survey on few good enhanced approaches of Apriori algorithm. This will be really very helpful for the upcoming researchers to find some new ideas from these approaches.
References
- Dr (Mrs).Sujni Paul Associate Professor Department of Computer Applications, Karunya University, Coimbatore 641114 , Tamil Nadu, India.
- María N. Moreno, Saddys Segrera and Vivian F. López Universidad de Salamanca, Plaza Merced S/N, 37008, Salamanca.
- K. Pazhani Kumar Assistant Professor Dept of Computer Science S.T. Hindu College S. Arumugaperumal Head Dept of Computer Science S.T. Hindu College
- W. Sun, M. Pan, and Y. Qiang, “Inproved association rule mining method based on t statistical,” Application Research of Computers. vol. 28, no. 6, pp. 2073-2076, Jun, 2011
- J. Han and M. Kamber, Conception and Technology of Data Mining, Beijing: China Machine Press, 2007.
- C. Wang, R. Li, and M. Fan, “Mining Positively Correlated FrequentItemsets,” Computer Applications, vol. 27, pp. 108-109, 2007
- J. Pei, J. Han, and H. Lu. Hmine: Hyper-structure mining of frequent patterns in large databases. In ICDM, 2001, pp441–448.
- Ms. Rina Raval, Prof. Indr Jeet Rajput , Prof. Vinitkumar Gupta, “Survey on several improved Apriori algorithms”, IOSR Journal of Computer Engineering (IOSR-JCE), Volume 9, Issue 4 (Mar. - Apr. 2013), PP 57-61
- Reeti Trikha, Jasmeet Singh, “improving the efficiency of apriori algorithm by adding new parameters”, International Journal for Multi Disciplinary Engineering and Business Management, Volume-2, Issue-2, June-2014
- Mohammed Al-Maolegi, Bassam Arkok, “An improved apriori algorithm for association rules”, International Journal on Natural Language Computing (IJNLC) Vol. 3, No.1, February 2014
- R. Agrawal, T. Imielinski, and A. Swami.. Mining association rules between sets of items in larged databases, In Proceedings of the 1993 ACM SIGMODInternational Connference on Management of Data, pages 207-216, Washington, DC, May 26-28-1993.
Index Terms
Keywords