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Reseach Article

An Efficient Approach for Parallel and Incremental Mining of Frequent Pattern in Transactional Database

Published on September 2015 by Pamli Basak, Rashmi Thakur
International Conference and Workshop on Communication, Computing and Virtualization
Foundation of Computer Science USA
ICWCCV2015 - Number 2
September 2015
Authors: Pamli Basak, Rashmi Thakur
bacccd80-e4d8-4770-9e92-853cf1ea4c8e

Pamli Basak, Rashmi Thakur . An Efficient Approach for Parallel and Incremental Mining of Frequent Pattern in Transactional Database. International Conference and Workshop on Communication, Computing and Virtualization. ICWCCV2015, 2 (September 2015), 0-0.

@article{
author = { Pamli Basak, Rashmi Thakur },
title = { An Efficient Approach for Parallel and Incremental Mining of Frequent Pattern in Transactional Database },
journal = { International Conference and Workshop on Communication, Computing and Virtualization },
issue_date = { September 2015 },
volume = { ICWCCV2015 },
number = { 2 },
month = { September },
year = { 2015 },
issn = 2249-0868,
pages = { 0-0 },
numpages = 1,
url = { /proceedings/icwccv2015/number2/794-1562/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Communication, Computing and Virtualization
%A Pamli Basak
%A Rashmi Thakur
%T An Efficient Approach for Parallel and Incremental Mining of Frequent Pattern in Transactional Database
%J International Conference and Workshop on Communication, Computing and Virtualization
%@ 2249-0868
%V ICWCCV2015
%N 2
%P 0-0
%D 2015
%I International Journal of Applied Information Systems
Abstract

In this paper, we provide an overview of parallel incremental association rule mining, which is one of the imminent ideas in the new and rapidly emerging research area of data mining. A useful tool for discovering frequently co-occurrent items is frequent itemset mining (FIM). Since its commencement, a number of significant FIM algorithms have been build up to increase mining performance. But when thedataset size is huge, both the computational cost and memory use can be toocostly. In this paper,we put frontward parallelizing the FP-Growth algorithm. We use MapReduce to execute the parallelization of FP-Growth algorithm. Henceforth, it splits the mining task into number of sub-tasks, implements these sub-tasks in parallel on nodes and then combines the results back for the final result. Experiments show that the result increases the computational speed as compared to apriori and fp-growth.

References
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Index Terms

Computer Science
Information Sciences

Keywords

FIM AIUA Parallel FP-growth Parallelized Incremental Mining Mapreduce Hadoop