CFP last date
20 June 2024
Reseach Article

Logical Itemset Mining Implementation on Hadoop

by Karan Jawalkar, Avinash Patil, Shreemay Panhalkar, Raj Pande
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 9
Year of Publication: 2016
Authors: Karan Jawalkar, Avinash Patil, Shreemay Panhalkar, Raj Pande
10.5120/ijca2016908280

Karan Jawalkar, Avinash Patil, Shreemay Panhalkar, Raj Pande . Logical Itemset Mining Implementation on Hadoop. International Journal of Computer Applications. 135, 9 ( February 2016), 1-3. DOI=10.5120/ijca2016908280

@article{ 10.5120/ijca2016908280,
author = { Karan Jawalkar, Avinash Patil, Shreemay Panhalkar, Raj Pande },
title = { Logical Itemset Mining Implementation on Hadoop },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 135 },
number = { 9 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-3 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume135/number9/24074-2016908280/ },
doi = { 10.5120/ijca2016908280 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:35:17.188038+05:30
%A Karan Jawalkar
%A Avinash Patil
%A Shreemay Panhalkar
%A Raj Pande
%T Logical Itemset Mining Implementation on Hadoop
%J International Journal of Computer Applications
%@ 0975-8887
%V 135
%N 9
%P 1-3
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Frequent Itemset Mining (FISM) finds the large and fre-quently occurring items from the datasets using Apri-ori algorithm. The FISM framework does not addresses two major properties that are Mixture-of property(more than one customer intent) and Projection-of property. To overcome the problems of irrelevant and non ac-tionable data and also to address the properties men-tioned above, Logical Itemset Mining (LISM) frame-work is introduced. LISM finds logical itemsets from the data which helps in eliminating non actionable data but at the same time keeps data which is log-ically connected. LISM not only finds logically con-nected items but aso items which are rarely occurring but logically connected are also discovered. LISM also addresses the Mixture of property and Projection of property which are not very well addressed in FISM.

References
  1. R. Agrawal, T. Imielinski, and A. N. Swami, Mining associa-tion rules between sets of items in large databases, SIGMOD, pp. 207216, 1993
  2. M. J. Zaki, Scalable algorithms for association mining, IEEE TKDE, vol. 12, pp. 372-390, 2000
  3. R. Agrawal and R. Srikant, Fast algorithms for mining m asso-ciation rules in large databases , VLDB, pp. 487499,1994.
  4. Shailesh Kumar, Chandrashekar V and C V Jawahar, Logical Itemset Mining , 2013.
  5. L. Szathmary, A. Napoli, and P. Valtchev, Towards rare itemset mining, ICTAI (1), pp. 305312, 2007.
  6. https://hasgeek.tv/skumar0127/speaking-in/638-mapreduce-and-the-art-of-thinking-parallel
  7. https://hasgeek.tv/skumar0127/speaking-in/640-co-occurrence-analytics-a-versatile-framework-for-finding-interesting-needles-in-crazy-haystacks
Index Terms

Computer Science
Information Sciences

Keywords

FISM LISM M/R Job FLASK LUCENE