Abstract
Mobile mining is about finding useful knowledge from the raw data produced by mobile users. The mobile environment consists of a set of static device and mobile device. Previous works in mobile data mining include finding frequency pattern and group pattern. Location dependency was not part of consideration in previous work but it would be meaningful. The proposed method builds a user profile based on past mobile visiting data, filters and to mine association rules. The more frequent the user profiles are updated, the more accurate the rules are. Our performance evaluation shows that as the number of characteristics increases, the number of rules will increase dramatically and therefore, a careful choosing of only the relevant characteristics to ensure acceptable amount of rules.
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Goh, J.Y., Taniar, D. (2004). Mobile Data Mining by Location Dependencies. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_33
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DOI: https://doi.org/10.1007/978-3-540-28651-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
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