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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/70679

Title:	Smartphone App Usage Log Mining
Authors:	徐國偉 Hsu, Kuo-Wei
Contributors:	資科系
Keywords:	Association rule minin;, sequential pattern mining;smartphone app usage log
Date:	2014.08
Issue Date:	2014-10-20 18:21:16 (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:	International Journal of Computer and Electrical Engineering (IJCEE), 6(2), 151-156
Data Type:	article
DOI 連結:	http://dx.doi.org/10.7763/IJCEE.2014.V6.812
DOI:	10.7763/IJCEE.2014.V6.812
Appears in Collections:	[資訊科學系] 期刊論文

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