學術產出-期刊論文
文章檢視/開啟
書目匯出
Google ScholarTM
政大圖書館
引文資訊
-
No data in Web of Science(Wrong one)No data in Scopus
TAIR相關學術產出
題名 | Smartphone App Usage Log Mining |
作者 | 徐國偉 Hsu, Kuo-Wei |
貢獻者 | 資科系 |
關鍵詞 | Association rule minin;, sequential pattern mining; smartphone app usage log |
日期 | 2014.08 |
上傳時間 | 20-十月-2014 18:21:16 (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 | International Journal of Computer and Electrical Engineering (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) | Hsu, Kuo-Wei | en_US |
dc.date (日期) | 2014.08 | en_US |
dc.date.accessioned | 20-十月-2014 18:21:16 (UTC+8) | - |
dc.date.available | 20-十月-2014 18:21:16 (UTC+8) | - |
dc.date.issued (上傳時間) | 20-十月-2014 18:21:16 (UTC+8) | - |
dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/70679 | - |
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 | 1448373 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en_US | - |
dc.relation (關聯) | International Journal of Computer and Electrical Engineering (IJCEE), 6(2), 151-156 | en_US |
dc.subject (關鍵詞) | Association rule minin;, sequential pattern mining; smartphone app usage log | en_US |
dc.title (題名) | Smartphone App Usage Log Mining | en_US |
dc.type (資料類型) | article | en |
dc.identifier.doi (DOI) | 10.7763/IJCEE.2014.V6.812 | en_US |
dc.doi.uri (DOI) | http://dx.doi.org/10.7763/IJCEE.2014.V6.812 | en_US |