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Analysis of Online User Behavior Detection Methodologies and its Evaluation

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Dhanashree Deshpande, Shrinivas Deshpande

Dhanashree Deshpande and Shrinivas Deshpande. Analysis of Online User Behavior Detection Methodologies and its Evaluation. International Journal of Computer Applications 161(3):1-5, March 2017. BibTeX

	author = {Dhanashree Deshpande and Shrinivas Deshpande},
	title = {Analysis of Online User Behavior Detection Methodologies and its Evaluation},
	journal = {International Journal of Computer Applications},
	issue_date = {March 2017},
	volume = {161},
	number = {3},
	month = {Mar},
	year = {2017},
	issn = {0975-8887},
	pages = {1-5},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017913126},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


With the increasing use of internet, users are accessing information and services easily through various media like social communication, multimedia content, online shopping and banking services etc. It becomes challenging task to accurately identify and differentiate normal and suspicious user behavior. Various businesses need information of next user behavior prediction to enhance their service quality. This paper gives the analysis of online user behavior detection and prediction. Various user behaviors identification methods are compared and analyzed. Their parameters are considered and improvements are suggested. The proposed methodology describes anomalous user behavior detection system. The principal component analysis is the feature extraction method used to detect and differentiate normal and anomalous user behavior.


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online user behavior; fuzzy theory; GSP algorithm; SGD algorithm; PCA