Welcome to the InfoSci Platform
Frequent Itemset Mining in Large Datasets a Survey

Frequent Itemset Mining in Large Datasets a Survey

Amrit Pal, Manish Kumar
Copyright: © 2017 |Volume: 7 |Issue: 4 |Pages: 13
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781522514343|DOI: 10.4018/IJIRR.2017100103
Cite Article Cite Article

MLA

Pal, Amrit, and Manish Kumar. "Frequent Itemset Mining in Large Datasets a Survey." IJIRR vol.7, no.4 2017: pp.37-49. http://doi.org/10.4018/IJIRR.2017100103

APA

Pal, A. & Kumar, M. (2017). Frequent Itemset Mining in Large Datasets a Survey. International Journal of Information Retrieval Research (IJIRR), 7(4), 37-49. http://doi.org/10.4018/IJIRR.2017100103

Chicago

Pal, Amrit, and Manish Kumar. "Frequent Itemset Mining in Large Datasets a Survey," International Journal of Information Retrieval Research (IJIRR) 7, no.4: 37-49. http://doi.org/10.4018/IJIRR.2017100103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules. The amount of data is increasing day by day taking form of BigData, which require changes in the algorithms for working on such large-scale data. Parallel implementation of the mining techniques can provide solutions to this problem. In this paper a survey of frequent itemset mining techniques is done which can be used in a parallel environment. Programming models like Map Reduce provides efficient architecture for working with BigData, paper also provides information about issues and feasibility about technique to be implemented in such environment.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.