Empirical study on B/C apparel consumption behavior based on Data Mining technology

Jian Fang Liang, Jianming Liang, Jian Ping Wang

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations


    In order to accurately identify the characters associated with consumption behavior of apparel online shopping, a typical B/C clothing enterprise in China was chosen. The target experimental database containing 2000 data records was obtained based on web service logs of sample enterprise. By means of clustering algorithm of Clementine Data Mining Software, K-means model was set up and 8 clusters of consumer were concluded. Meanwhile, the implicit information existed in consumer's characters and preferences for clothing was found. At last, 31 valuable association rules among casual wear, formal wear, and tie-in products were explored by using web analysis and Aprior algorithm. This finding will help to better understand the nature of online apparel consumption behavior and make a good progress in personalization and intelligent recommendation strategies.

    Original languageEnglish (US)
    Pages (from-to)530-536
    Number of pages7
    JournalJournal of Donghua University (English Edition)
    Issue number6
    StatePublished - Dec 2013


    • Apparel industry
    • Consumption behavior
    • Data mining
    • Online shopping

    ASJC Scopus subject areas

    • Polymers and Plastics
    • Industrial and Manufacturing Engineering


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