Semantic Based Weighted Web Session Clustering Using Adapted K-Means and Hierarchical Agglomerative Algorithms

Authors

  • Sowmya HK Department of Information Science and Engineering, New Horizon College of Engineering, Affiliated to Visvesvaraya Technological University, Bengaluru, India https://orcid.org/0000-0002-1082-659X
  • R. J. Anandhi Department of Information Science and Engineering, New Horizon College of Engineering, Affiliated to Visvesvaraya Technological University, Bengaluru, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2125

Keywords:

sessionization, dissimilarity matrix, session weight, session cluster, cluster evaluation.

Abstract

The WWW has a big number of pages and URLs that supply the user with a great amount of content. In an intensifying epoch of information, analysing users browsing behaviour is a significant affair. Web usage mining techniques are applied to the web server log to analyse the user behaviour. Identification of user sessions is one of the key and demanding tasks in the pre-processing stage of web usage mining. This paper emphasizes on two important fallouts with the approaches used in the existing session identification methods such as Time based and Referrer based sessionization. The first is dealing with comparing of current request’s referrer field with the URL of previous request. The second is dealing with session creation, new sessions are created or comes in to one session due to threshold value of page stay time and session time. So, authors developed enhanced semantic distance based session identification algorithm that tackles above mentioned issues of traditional session identification methods. The enhanced semantic based method has an accuracy of 84 percent, which is higher than the Time based and Time-Referrer based session identification approaches. The authors also used adapted K-Means and Hierarchical Agglomerative clustering algorithms to improve the prediction of user browsing patterns. Clusters were found using a weighted dissimilarity matrix, which is calculated using two key parameters: page weight and session weight. The Dunn Index and Davies-Bouldin Index are then used to evaluate the clusters. Experimental results shows that more pure and accurate session clusters are formed when adapted clustering algorithms are applied on the weighted sessions rather than the session obtained from traditional sessionization algorithms. Accuracy of the semantic session cluster is higher compared with the cluster of sessions obtained using traditional sessionization.

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Author Biographies

Sowmya HK, Department of Information Science and Engineering, New Horizon College of Engineering, Affiliated to Visvesvaraya Technological University, Bengaluru, India

Sowmya HK received the Bachelor of Engineering degree in Computer Science and Engineering from Kurunji Venkataramana Gowda College of Engineering in 2004, the Master of Engineering degree in Computer Science and Engineering from University Visvesvaraya College of Engineering, Bangalore University in 2010 respectively. She is currently working as Senior Assistant Professor at the Department of Artificial Intelligence and Machine Learning, New Horizon College of Engineering, Visvesvaraya Technological University. She is currently pursuing Ph.D at the Department of Information Science and Engineering, New Horizon College of Engineering, Visvesvaraya Technological University. Her research areas include Web Usage Mining, Data Mining, and Deep Learning.

R. J. Anandhi, Department of Information Science and Engineering, New Horizon College of Engineering, Affiliated to Visvesvaraya Technological University, Bengaluru, India

R. J. Anandhi received the Bachelor of Engineering degree in Computer Science and Engineering from Government College of Technology, Coimbatore in 1991. She secured Master of Technology degree in Computer Science and Engineering from Pondicherry Central University in 1995. She pursued Ph.D from Dr. MGR University, Chennai in 2011. She is currently working as Professor and Head of the Department of Information Science & Engineering at New Horizon College of Engineering, Bengaluru. Her research areas include Data Mining, NLP and Cloud Computing.

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http://www.almhuette-raith.at/apache-log/access.log

Published

2022-01-04

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Articles