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Analysis of E-Commerce Big Data using Clustering and CloudSim Load Balancing

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Neha Jain, Anil Suryavanshi

Neha Jain and Anil Suryavanshi. Analysis of E-Commerce Big Data using Clustering and CloudSim Load Balancing. International Journal of Computer Applications 161(11):50-54, March 2017. BibTeX

	author = {Neha Jain and Anil Suryavanshi},
	title = {Analysis of E-Commerce Big Data using Clustering and CloudSim Load Balancing},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {161},
	number = {11},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {50-54},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017913327},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In this paper an efficient technique is implemented for the analysis of E-Commerce based Applications over Big Data. The Proposed Methodology implemented here is based on the concept of providing Extracting Feature Vectors from the E-Commerce Data and Load balancing of Data using CloudSim based Load balancing and finally Clustered the Data. The Proposed Methodology implemented provides efficient Accuracy & Processing Time as compared to the existing methodology implemented for the analysis of E-Commerce Data.


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Big-Data, E-Commerce Data, Hadoop, CloudSim, Clustering, Load Balancing, Feature Vectors.