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Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
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Fast Mining of Finding Frequent Patterns in Transactional Database using Incremental Approach

by Pamli Basak, R.r. Sedamkar, Rashmi Thakur
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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.

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