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題名 Smartphone App Usage Log Mining
作者 徐國偉
Tseng,Woan-Rou;Hsu,Kuo-Wei
貢獻者 資科系
關鍵詞 Association rule mining; sequential pattern mining; smartphone app usage log
日期 2014-04
上傳時間 21-Aug-2014 14:47:00 (UTC+8)
摘要 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.
關聯 IJCEE,6(2),151-156
資料類型 article
DOI http://dx.doi.org/10.7763/IJCEE.2014.V6.812
dc.contributor 資科系en_US
dc.creator (作者) 徐國偉zh_TW
dc.creator (作者) Tseng,Woan-Rou;Hsu,Kuo-Weien_US
dc.date (日期) 2014-04en_US
dc.date.accessioned 21-Aug-2014 14:47:00 (UTC+8)-
dc.date.available 21-Aug-2014 14:47:00 (UTC+8)-
dc.date.issued (上傳時間) 21-Aug-2014 14:47:00 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/69121-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 105 bytes-
dc.format.mimetype text/html-
dc.language.iso en_US-
dc.relation (關聯) IJCEE,6(2),151-156en_US
dc.subject (關鍵詞) Association rule mining; sequential pattern mining; smartphone app usage logen_US
dc.title (題名) Smartphone App Usage Log Miningen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.7763/IJCEE.2014.V6.812-
dc.doi.uri (DOI) http://dx.doi.org/10.7763/IJCEE.2014.V6.812-