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Reseach Article

Pri-Tri: An Innovative Algorithm for Clustering Categorical Data in Data Warehouse

by S.Hari Ganesh, C.Chandrasekar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 20 - Number 7
Year of Publication: 2011
Authors: S.Hari Ganesh, C.Chandrasekar
10.5120/2448-3307

S.Hari Ganesh, C.Chandrasekar . Pri-Tri: An Innovative Algorithm for Clustering Categorical Data in Data Warehouse. International Journal of Computer Applications. 20, 7 ( April 2011), 6-11. DOI=10.5120/2448-3307

@article{ 10.5120/2448-3307,
author = { S.Hari Ganesh, C.Chandrasekar },
title = { Pri-Tri: An Innovative Algorithm for Clustering Categorical Data in Data Warehouse },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 20 },
number = { 7 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume20/number7/2448-3307/ },
doi = { 10.5120/2448-3307 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:07:07.865988+05:30
%A S.Hari Ganesh
%A C.Chandrasekar
%T Pri-Tri: An Innovative Algorithm for Clustering Categorical Data in Data Warehouse
%J International Journal of Computer Applications
%@ 0975-8887
%V 20
%N 7
%P 6-11
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the process of data mining to extract knowledge from large data set needs great potential to extract the hidden nuggets. To cluster the numerical data there are enormous clustering technique. Data mining for categorical data(qualitative and quantitative) the most frequently used algorithms are k-means, k-mediods and fuzzy rule all these methods needs a threshold value to overcome this problem. This paper propose an algorithm to optimize the number of clusters and it also uses novel way to construct the data mart using the concept of multiprocessing Pri-tri algorithm.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining clustering k-means Multiprocessing