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Reseach Article

Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach

by Pamli Basak, R.r. Sedamkar, Rashmi Thakur
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 2
Year of Publication: 2015
Authors: Pamli Basak, R.r. Sedamkar, Rashmi Thakur
10.5120/ijais15-451369

Pamli Basak, R.r. Sedamkar, Rashmi Thakur . Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach. International Journal of Applied Information Systems. 9, 2 ( June 2015), 6-10. DOI=10.5120/ijais15-451369

@article{ 10.5120/ijais15-451369,
author = { Pamli Basak, R.r. Sedamkar, Rashmi Thakur },
title = { Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach },
journal = { International Journal of Applied Information Systems },
issue_date = { June 2015 },
volume = { 9 },
number = { 2 },
month = { June },
year = { 2015 },
issn = { 2249-0868 },
pages = { 6-10 },
numpages = {9},
url = { https://www.ijais.org/archives/volume9/number2/754-1369/ },
doi = { 10.5120/ijais15-451369 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:59:44.672306+05:30
%A Pamli Basak
%A R.r. Sedamkar
%A Rashmi Thakur
%T Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 9
%N 2
%P 6-10
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Datasets grow in size as they are increasingly being gathered by cheap and numerous information-sensing mobile devices, aerial, software logs, microphones, wireless sensor networks and cameras. This paper presents a structure for simply, easily and competently parallelizing data mining algorithms for those huge datasets together with the incremental mining. MapReduce concept is use to execute the parallel FP-Growth algorithm by running the windows services parallel. The proposed algorithm eliminates duplicated work and spurious items. Also, it shortens the response time to a query for the set of frequent items. The proposed algorithm is implemented by parallel running of many windows services and experimental results shows tremendous advantages. The proposed algorithm runs 66% faster than the traditional algorithm of data mining. Also, memory utilization reduces by 37%.

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Index Terms

Computer Science
Information Sciences

Keywords

Incremental Data Mining Parallel FP-growth MapReduce jobs Incremental Parallel FP-growth.