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

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Parneet Kaur, Kamaljit Kaur
10.5120/ijca2016909303

Parneet Kaur and Kamaljit Kaur. Article: Clustering Techniques in Data Mining For Improving Software Architecture: A Review. International Journal of Computer Applications 139(9):35-39, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Parneet Kaur and Kamaljit Kaur},
	title = {Article: Clustering Techniques in Data Mining For Improving Software Architecture: A Review},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {9},
	pages = {35-39},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

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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Keywords

Clustering, Software Engineering, k-means, Outliers.