Similarity Matrix Based Session Clustering by Sequence Alignment Using Dynamic Programming | Zendy

K. Duraiswamy | Zendy; V. Valli Mayil | Zendy

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Similarity Matrix Based Session Clustering by Sequence Alignment Using Dynamic Programming

Author(s) -

K. Duraiswamy,

V. Valli Mayil

Publication year - 2008

Publication title -

computer and information science

Language(s) - English

Resource type - Journals

eISSN - 1913-8997

pISSN - 1913-8989

DOI - 10.5539/cis.v1n3p66

Subject(s) - computer science , session (web analytics) , cluster analysis , web mining , hierarchical clustering , similarity (geometry) , information retrieval , world wide web , data mining , web service , database transaction , database , machine learning , artificial intelligence , image (mathematics)

With the rapid increasing popularity of the WWW, Websites are playing a crucial role to convey knowledge to the end users. Every request of Web site or a transaction on the server is stored in a file called server log file. Providing Web administrator with meaningful information about user access behavior (also called click stream data) has become a necessity to improve the quality of Web information and service performance. As such, the hidden knowledge obtained from mining, web server traffic data and user access patterns ( called Web Usage Mining), could be directly used for marketing and management of E-business, E-services, E-searching , E-education and so on.

Categorizing visitors or users based on their interaction with a web site is a key problem in web usage mining. The click stream generated by various users often follows distinct patterns, clustering of the access pattern will provide the knowledge, which may help in recommender system of finding learning pattern of user in E-learning system , finding group of visitors with similar interest , providing customized content in site manager, categorizing customers in E-shopping etc.

Given session information, this paper focuses a method to find session similarity by sequence alignment using dynamic programming, and proposes a model such as similarity matrix for representing session similarity measures. The work presented in this paper follows Agglomerative Hierarchical Clustering method to cluster the similarity matrix in order to group similar sessions and the clustering process is depicted in dendrogram diagram.

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