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Analysis of Web Performance based on Navigation Pattern using Progressive Web Datasets

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Sapana Kumari, Vikram Garg
10.5120/ijca2016911091

Sapana Kumari and Vikram Garg. Analysis of Web Performance based on Navigation Pattern using Progressive Web Datasets. International Journal of Computer Applications 148(4):34-36, August 2016. BibTeX

@article{10.5120/ijca2016911091,
	author = {Sapana Kumari and Vikram Garg},
	title = {Analysis of Web Performance based on Navigation Pattern using Progressive Web Datasets},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2016},
	volume = {148},
	number = {4},
	month = {Aug},
	year = {2016},
	issn = {0975-8887},
	pages = {34-36},
	numpages = {3},
	url = {http://www.ijcaonline.org/archives/volume148/number4/25748-2016911091},
	doi = {10.5120/ijca2016911091},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Now a day’s the e-commerce websites are facing the biggest challenges of the massive growth of website data. There is a need to find behavior of user so that there is need to find next page in advance on cache. Further they do not have a good policy for finding user behavior in website. They need to use good approach for improving the quality and accuracy in today’s scenario. Closed sequential pattern mining is an important technique among the different types of sequential pattern mining, since it preserves the details of the full pattern set and it is more compact than sequential pattern mining. In this paper the clustering task is used to improve performance of website navigation pattern in advance. The main goal of this research is to find the extract the knowledge that can enhance web performance of associate items in sequential manner with the quality.

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Keywords

e-commerce datasets, knowledge mining, Decision Making, Data Classification, Performance Prediction.