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Reseach Article

Clustering Techniques in Data Mining For Improving Software Architecture: A Review

by Parneet Kaur, Kamaljit Kaur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 9
Year of Publication: 2016
Authors: Parneet Kaur, Kamaljit Kaur

Parneet Kaur, Kamaljit Kaur . Clustering Techniques in Data Mining For Improving Software Architecture: A Review. International Journal of Computer Applications. 139, 9 ( April 2016), 35-39. DOI=10.5120/ijca2016909303

@article{ 10.5120/ijca2016909303,
author = { Parneet Kaur, Kamaljit Kaur },
title = { Clustering Techniques in Data Mining For Improving Software Architecture: A Review },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 9 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016909303 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:40:31.308448+05:30
%A Parneet Kaur
%A Kamaljit Kaur
%T Clustering Techniques in Data Mining For Improving Software Architecture: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 9
%P 35-39
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

Data mining is a set of problem solving skills, instructions and methods applied upon variety of domains to discover and create useful systems that are used to solve practical problems. Clustering technique defines classes and put objects which are related to them in one class on the other hand in classification objects are placed in predefined classes. There are many clustering techniques for the improvement of architecture which are discussed in this paper. This paper also gives comparative study of clustering techniques and addresses benefits and limitations of clustering techniques.

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Index Terms

Computer Science
Information Sciences


Clustering Software Engineering k-means Outliers.