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Generating Frequent Item Sets Using Apache Hadoop Map Reduce and Mahout

Barooru Sasanka Kasyap1 , K.Syama Sundara Rao2

Section:Research Paper, Product Type: Journal Paper
Volume-3 , Issue-10 , Page no. 43-47, Oct-2015

Online published on Oct 31, 2015

Copyright © Barooru Sasanka Kasyap , K.Syama Sundara Rao . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Barooru Sasanka Kasyap , K.Syama Sundara Rao , “Generating Frequent Item Sets Using Apache Hadoop Map Reduce and Mahout,” International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.43-47, 2015.

MLA Style Citation: Barooru Sasanka Kasyap , K.Syama Sundara Rao "Generating Frequent Item Sets Using Apache Hadoop Map Reduce and Mahout." International Journal of Computer Sciences and Engineering 3.10 (2015): 43-47.

APA Style Citation: Barooru Sasanka Kasyap , K.Syama Sundara Rao , (2015). Generating Frequent Item Sets Using Apache Hadoop Map Reduce and Mahout. International Journal of Computer Sciences and Engineering, 3(10), 43-47.

BibTex Style Citation:
@article{Kasyap_2015,
author = {Barooru Sasanka Kasyap , K.Syama Sundara Rao },
title = {Generating Frequent Item Sets Using Apache Hadoop Map Reduce and Mahout},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2015},
volume = {3},
Issue = {10},
month = {10},
year = {2015},
issn = {2347-2693},
pages = {43-47},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=701},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=701
TI - Generating Frequent Item Sets Using Apache Hadoop Map Reduce and Mahout
T2 - International Journal of Computer Sciences and Engineering
AU - Barooru Sasanka Kasyap , K.Syama Sundara Rao
PY - 2015
DA - 2015/10/31
PB - IJCSE, Indore, INDIA
SP - 43-47
IS - 10
VL - 3
SN - 2347-2693
ER -

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Abstract

The Item set Mining is one of the most well known techniques to extract knowledge from data. The mechanism having some problematic data, for those further enhancements have been applied based on the Big Data in which some performances has-been explores on Map Reduce machine. The new approach for mining large datasets such as K-means, Mahout which targets on speed and time while Big FIM is optimized to run on really large datasets. K-means algorithm depends on Map Reduce, which is the infrastructure for prepare more datasets of certain scattered clusters. These clusters are combined in the form of nodes and edges and also display the item sets. The execution of these clusters can also be implemented on large datasets also with high scalability and performance.

Key-Words / Index Term

Big Data;Hadoop; Map Reduce;Mahout;K-Means

References

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[2] Apache Mahout, http://mahout.apache.org/ , 2014.
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