NDLI: A webpage similarity measure for web sessions clustering using sequence alignment

Content Provider	IEEE Xplore Digital Library
Author	Azimpour-Kivi, M. Azmi, R.
Copyright Year	2011
Description	Author affiliation: School of Engineering and Science, Sharif University of Technology, International Campus, Kish Island, Iran (Azimpour-Kivi, M.) \|\| Department of Computer Engineering, Alzahra University, Tehran, Iran (Azmi, R.)
Abstract	Web sessions clustering is a process of web usage mining task that aims to group web sessions with similar trends and usage patterns into clusters. This process is crucial for effective website management, web personalization and developing web recommender systems. Accurate clustering of web sessions is highly dependent to the similarity measure defined to compare web sessions. In this paper, we propose a similarity measure for comparing web sessions. The sequential order of web navigations in sessions is considered using sequence alignment method. Furthermore, we propose to consider the usage similarity of two web sessions based on the time a user spends on a webpage, and also the frequency of visit of each page within the session. The proposed method is validated by clustering a collection of web sessions using an agglomerative clustering technique and comparing the results with available methods. The experimental results show effectiveness of the proposed method to capture the properties of web session data.
Starting Page	20
Ending Page	24
File Size	192824
Page Count	5
File Format	PDF
ISBN	9781424498338
e-ISBN	9781424498345
DOI	10.1109/AISP.2011.5960993
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-15
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Couplings Web sessions clustering Conferences Web pages Clustering algorithms Sequence alignment Webpage simialrity measure Time frequency analysis Interestingness of webpage Dynamic programming Data mining
Content Type	Text
Resource Type	Article

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