Research Aligned Analysis on Web Access Behavioral Pattern Mining for User Identification
Gokulapriya R1, Ganesh Kumar R.2
1Gokulapriya R., Research Scholar, Computer Science and Engineering, Christ (Deemed to be University), India.
2Ganesh Kumar R., Associate Professor, Computer Science and Engineering, CHRIST (Deemed to be University), India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 5062-5067 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9552088619/2019©BEIESP | DOI: 10.35940/ijeat.F9552.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human activity understanding includes activity recognition and activity pattern discovery. Monitoring human activity and finding abnormality in their activities used by many field like medical applications, security systems etc. Basically it helps and support in decision making systems. Mining user activity from web logs can helps in finding hidden information about the user access pattern which reveals the web access behaviour of the users. Clustering and Classification techniques are used for web user identification. Clustering is the task of grouping similar patterns for web user identification. Classification is the process of classifying web patterns for user identification. In this paper we have implemented the existing works and discussed the results here to find the limitations. In existing methods, many data mining techniques were introduced for web user behaviour identification. But, the user identification accuracy was not improved and time consumption was not reduced. Our objective is to study the existing work and explore the possibility to improve the identification accuracy and reduce the time consumption using machine learning and deep learning techniques.
Keywords: Human activity understanding, web usage mining, web user behavior, data mining, web patterns, User Idenifiction.