Front Inner Page - Volume 4 No.5 October 2017

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  • Title

    :

    Similarity Measurement Of Web Navigation Pattern Using K-Harmonic Mean Algorithm.

    Authors

    :

    K.Abirami 1,Dr. P.Mayilvaganan 2

    Keywords

    :

    Web Data Mining; Pattern Discover; Web Log data; Classification of Users, Association Rules, clustering algorithm(KHM).

    Issue Date

    :

    October 2017

    Abstract

    :

    we present a new method to improve the web Navigation Usage Pattern to discover the web data based on similarity between two cluster points. The web usage patterns can be extracted from Web server logs regularly verified for working websites by first handling the log data to find users, user sessions, and user task-oriented transactions, and then applying a Web usage mining algorithm to determine patterns among web usage paths. In conventional Web usage mining, semantic information of the Web page content does not take part in the pattern generation process. The web navigation usage pattern including information about both the path and time essential for user-oriented tasks. It is taken by our ideal user communicating path models. It can be measure to distance between similar web usage patterns. In this approach, the user visited pages are subdivided into clusters using a non-Euclidean distance measure called the Sequence Order Method (SOM) and Euclidean method measure called Association Distance Measure (ADM). In this paper mainly focus to identify page path similarity, and implementing KHM clustering algorithm. The minimum number of pages in a session and similarity of usage path were calculated.

    Page(s)

    :

    1-6

    ISSN

    :

    2347- 4734

    Source

    :

    Vol. 4, No.5, October 2017

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