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Comparative Clustering Approach Intended for Evaluating SaaS

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Dhanamma Jagli, Seema Purohit, N. Subhash Chandra
10.5120/ijca2017914679

Dhanamma Jagli, Seema Purohit and Subhash N Chandra. Comparative Clustering Approach Intended for Evaluating SaaS. International Journal of Computer Applications 169(8):13-16, July 2017. BibTeX

@article{10.5120/ijca2017914679,
	author = {Dhanamma Jagli and Seema Purohit and N. Subhash Chandra},
	title = {Comparative Clustering Approach Intended for Evaluating SaaS},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {8},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {13-16},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume169/number8/28003-2017914679},
	doi = {10.5120/ijca2017914679},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

The Cloud computing is key computing and playing an important role to share many resources. The Software as a service (SaaS) service model is giving more benefits to the end user. Any user wants to use the software as a service, will not install any software locally rather they can access any application/software as assertive via the internet just by login to the system. For every service user, selecting the suitable one became a difficult task. Hence in this paper providing various data mining clustering algorithms and their comparison based AWS data sets collected. The goal of this research is to provide a decision-making system with optimal solutions to cloud service users and providers to select/provide potential software services and afford according to specified requirements quickly and easily.

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Keywords

SaaS, K-means Clustering, Hierarchical Clustering, BiCluster, Cloud Computing