CFP last date
20 March 2024
Reseach Article

A Survey on Achieving Best Knowledge from Frequent Item set Mining using Fidoop

by Sandhya S. Waghere, Pothuraju Rajarajeswari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 171 - Number 9
Year of Publication: 2017
Authors: Sandhya S. Waghere, Pothuraju Rajarajeswari
10.5120/ijca2017915068

Sandhya S. Waghere, Pothuraju Rajarajeswari . A Survey on Achieving Best Knowledge from Frequent Item set Mining using Fidoop. International Journal of Computer Applications. 171, 9 ( Aug 2017), 16-18. DOI=10.5120/ijca2017915068

@article{ 10.5120/ijca2017915068,
author = { Sandhya S. Waghere, Pothuraju Rajarajeswari },
title = { A Survey on Achieving Best Knowledge from Frequent Item set Mining using Fidoop },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2017 },
volume = { 171 },
number = { 9 },
month = { Aug },
year = { 2017 },
issn = { 0975-8887 },
pages = { 16-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume171/number9/28209-2017915068/ },
doi = { 10.5120/ijca2017915068 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:18:59.010006+05:30
%A Sandhya S. Waghere
%A Pothuraju Rajarajeswari
%T A Survey on Achieving Best Knowledge from Frequent Item set Mining using Fidoop
%J International Journal of Computer Applications
%@ 0975-8887
%V 171
%N 9
%P 16-18
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining mostly use for data analysis and identifying frequent dataset. Now a days cloud computing is used for data storage and many other data operations like data mining, data retrieval, data distribution etc. As data increasing very rapidly on server day by day, many complications are introduced. Most common problems are load balancing on server and time optimization. To overcome these limitations parallel frequent dataset mining is very effective method. Fidoop parallel frequent dataset mining algorithm which is based on mapreduce framework helps to improve load balancing and FiDoop-HD, speed up the mining performance for high-dimensional data analysis. Fidoop is very efficient and scalable algorithm for large clusters of data.

References
  1. D. Chen et al., “Tree partition based parallel frequent pattern mining on shared memory systems,” in Proc.20th IEEE Int. Parallel Distrib. Process. Symp.(IPDPS), Rhodes Island, Greece, 2006, pp. 1–8.
  2. Y.-J. Tsay, T.-J. Hsu, and J.-R. Yu, “FIUT: A newmethod for mining frequent itemsets,” Inf. Sci., vol.179, no. 11, pp. 1724–1737, 2009.
  3. E.-H. Han, G. Karypis, and V. Kumar, “Scalableparallel data mining for association rules,” IEEE Trans. Knowl. Data Eng., vol. 12, no. 3, pp. 337–352,May/Jun. 2000.
  4. K.-M. Yu, J. Zhou, T.-P. Hong, and J.-L. Zhou, “Aload-balanced Distributed parallel mining algorithm,”Expert Syst. Appl., vol. 37, no. 3, pp. 2459–2464, 2010.
  5. L. Zhou et al., “Balanced parallel FP-growth withMapReduce,” in Proc. IEEE Youth Conf. Inf. Comput. Telecommun. (YC-ICT), Beijing, China, 2010, pp.243–246.
  6. “ECLAT Algorithm for Frequent Itemsets Generation”ManjitkaurUrvashi Grag Computer Science and Technology, Lovely Professional University Phagwara, Punjab, India . InternationalJournal of Computer Systems (ISSN: 2394-1065), Volume 01– Issue 03, December, 2014 Available at http://www.ijcsonline.com/
  7. “Implementation Of Parallel Apriori Algorithm On Hadoop Cluster”A. Ezhilvathani1, Dr. K. Raja. International Journal of ComputerScience and Mobile Computing.
  8. M. Chen, X. Gao and H. Li, "An efficient parallel FP-Growth algorithm," 2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Zhangijajie, 2009,pp.283-286.DOI: 10.1109/CYBERC.2009.5342148
Index Terms

Computer Science
Information Sciences

Keywords

Frequent item sets Frequent Items Ultrametric trees Hadoop MapReduce.