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An Effective and Optimized Approach to Association Rule Mining using GPGPU

Milind Kamath1 , Ankit Katariya2 , Gaurav Bhokare3

  1. Dept. of Computer Engineering, PES Modern College of Engineering, Pune, India.
  2. Dept. of Computer Engineering, PES Modern College of Engineering, Pune, India.
  3. Dept. of Computer Engineering, PES Modern College of Engineering, Pune, India.

Correspondence should be addressed to: milindkamath10@gmail.com.

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-6 , Page no. 269-272, Jun-2017

Online published on Jun 30, 2017

Copyright © Milind Kamath, Ankit Katariya, Gaurav Bhokare . 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: Milind Kamath, Ankit Katariya, Gaurav Bhokare, “An Effective and Optimized Approach to Association Rule Mining using GPGPU,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.269-272, 2017.

MLA Style Citation: Milind Kamath, Ankit Katariya, Gaurav Bhokare "An Effective and Optimized Approach to Association Rule Mining using GPGPU." International Journal of Computer Sciences and Engineering 5.6 (2017): 269-272.

APA Style Citation: Milind Kamath, Ankit Katariya, Gaurav Bhokare, (2017). An Effective and Optimized Approach to Association Rule Mining using GPGPU. International Journal of Computer Sciences and Engineering, 5(6), 269-272.

BibTex Style Citation:
@article{Kamath_2017,
author = {Milind Kamath, Ankit Katariya, Gaurav Bhokare},
title = {An Effective and Optimized Approach to Association Rule Mining using GPGPU},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2017},
volume = {5},
Issue = {6},
month = {6},
year = {2017},
issn = {2347-2693},
pages = {269-272},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1338},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1338
TI - An Effective and Optimized Approach to Association Rule Mining using GPGPU
T2 - International Journal of Computer Sciences and Engineering
AU - Milind Kamath, Ankit Katariya, Gaurav Bhokare
PY - 2017
DA - 2017/06/30
PB - IJCSE, Indore, INDIA
SP - 269-272
IS - 6
VL - 5
SN - 2347-2693
ER -

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Abstract

Frequent Pattern Growth (FP-Growth) is a data mining technique, FP-growth algorithm introduced frequent pattern tree (FP-tree), stored as frequent item-sets in a compressed way. It overcomes drawback of candidate generation approach of multiple database scan but at the same time the transaction identifiers can be quite long taking substantial memory space and computation time. An optimised data structure viz. the Multi-Path Graph is used to improve the utilization and increase the efficiency of data mining techniques. Here we will be using graph as a data structure for storing frequent patterns in the memory. The graph structure will help to mine these frequent patterns without constructing FP-trees. However FP-Growth and MP-Graph fail to process extremely vast data-sets optimally. So we will be attempting to compare FP-Growth with MP-Graph as per its efficiency and memory utilization capability using parallelization techniques. We will try to achieve parallelization using CUDA, and bring forth a comparison of both the mining techniques.

Key-Words / Index Term

Associative rule mining , heterogenous parallel programming , CUDA , frequent pattern mining

References

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