Markov chain-like model for prediction service based on improved hierarchical particle swarm optimization cluster algorithm

Zhifang Liao, Min Liu, Tianhui Song, Li Kuang, Yan Zhang, Zhining Liao

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)

    Abstract

    Since web pages visited by users contain a variety of data resources and the clustering algorithms frequently used for web data do not take the heterogeneous nature into account when processing the heterogeneous data, this paper proposes a new algorithm, namely IHPSOC algorithm, to cluster web log data on the basis of web log mining. Based on particle swarm optimization (PSO), IHPSOC algorithm clusters the web log data through particle swarm iteration. Based on clustering results, this paper establishes Markov chain-like models which create a corresponding Markov chain for users in each different category so as to predict the web resources in users' need. The results of the experiments show that the proposed model gives better predication.

    Original languageEnglish
    Pages (from-to)653-674
    Number of pages22
    JournalInternational Journal of Software Engineering and Knowledge Engineering
    Volume26
    Issue number4
    DOIs
    Publication statusPublished - May 2016

    Keywords

    • Data clustering
    • Markov chain-like model
    • particle swarm optimization
    • web log mining

    ASJC Scopus subject areas

    • Software
    • Computer Networks and Communications
    • Computer Graphics and Computer-Aided Design
    • Artificial Intelligence

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