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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/69121


    Title: Smartphone App Usage Log Mining
    Authors: 徐國偉
    Tseng,Woan-Rou;Hsu,Kuo-Wei
    Contributors: 資科系
    Keywords: Association rule mining;sequential pattern mining;smartphone app usage log
    Date: 2014-04
    Issue Date: 2014-08-21 14:47:00 (UTC+8)
    Abstract: The rise of smartphones can be connected to the large number of applications, or apps, which can be installed and run by users on smartphones. It becomes important for researchers, smartphone designers, and application developers to know how users use apps on their smartphones. The aim of this paper is to present our work where data mining is applied to smartphone app usage log data with a focus on data preparation. The methods include association rule mining and sequential pattern mining. The results are the discovered rules and patterns that reflect users’ real-life app-using behaviors; they can lead to improved user interfaces, and they can also be used to reconstruct the context in which a user used his or her smartphone. We demonstrate an application of data mining that can have an impact on the smartphone industry, and we also demonstrate a data-driven approach that can help us know more about how users use apps on their smartphones.
    Relation: IJCEE,6(2),151-156
    Data Type: article
    DOI link: http://dx.doi.org/10.7763/IJCEE.2014.V6.812
    DOI: 10.7763/IJCEE.2014.V6.812
    Appears in Collections:[Department of Computer Science ] Periodical Articles

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