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RCRDE: A Method for Reducing the Rate of Re-Clustering, using Replicated Data Eliminate Algorithm

by Fateme Rashidi, Arash Ghorbannia Delavar, Fateme Heidari Soureshjani, Ali Broumandnia
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 25
Year of Publication: 2013
Authors: Fateme Rashidi, Arash Ghorbannia Delavar, Fateme Heidari Soureshjani, Ali Broumandnia
10.5120/12128-8472

Fateme Rashidi, Arash Ghorbannia Delavar, Fateme Heidari Soureshjani, Ali Broumandnia . RCRDE: A Method for Reducing the Rate of Re-Clustering, using Replicated Data Eliminate Algorithm. International Journal of Computer Applications. 69, 25 ( May 2013), 13-20. DOI=10.5120/12128-8472

@article{ 10.5120/12128-8472,
author = { Fateme Rashidi, Arash Ghorbannia Delavar, Fateme Heidari Soureshjani, Ali Broumandnia },
title = { RCRDE: A Method for Reducing the Rate of Re-Clustering, using Replicated Data Eliminate Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 25 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 13-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number25/12128-8472/ },
doi = { 10.5120/12128-8472 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:31:17.046902+05:30
%A Fateme Rashidi
%A Arash Ghorbannia Delavar
%A Fateme Heidari Soureshjani
%A Ali Broumandnia
%T RCRDE: A Method for Reducing the Rate of Re-Clustering, using Replicated Data Eliminate Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 25
%P 13-20
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper is explored a way to reduce therate of re-clustering andspeed uptheclusteringprocess oncategoricaltime-evolving data. This method introducestwoalgorithmsRDE (Replicated Data Elimination) andRCRDE. The RDEalgorithmremoves the successivesurveysof replicated dataandconsiders counters tokeepthis data. Hence the number of created windows via thesliding window techniqueis limited and thisleads todecrease thenumber ofimplementations ofclusteringalgorithm. The RCRDEalgorithmbased on MARDL (MAximal Resemblance Data Labeling) framework decidesabout re-clustering implementation ormodificationofpreviousclusteringresults. Thepresentedmethodisindependent of clusteringalgorithm'stype and any kind ofcategoricalclusteringalgorithmcan be used. According tothe results obtainedondifferentdata sets,this method performs well in practice and facilitatestheclustering implementationon categorical data. Also, this method can be utilized to cluster a very large categorical static databasewith higher quality than previous work.

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Index Terms

Computer Science
Information Sciences

Keywords

Categorical time-evolving data clustering data labeling drifting-concept detecting

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