Abstract
With the popularity of mobile phones, it is necessary to mine and analyze the user habits, network applications and other data, which can help provide users with a strong adaptability of information services. On the basis of acceleration sensor and touch screen data, we analyze the behaviors of browsing the web, chatting, making calls and playing game. The traditional Affinity Propagation algorithm analyzes all the characteristics of the data as an equal role in group behavior analysis, which has some limitations. In this paper, an Adaptive Feature Weighting based on Affinity Propagation (AFWAP) Group Behavior Analysis Algorithm is proposed, which introduces feature weight into the AP algorithm. The proposed method makes different contribution to the class center in each iteration process, and assigns a new weight for each dimension attribute then to update the feature weight adaptively. In the clustering process, the importance of different features can be measured, which solves the shortcomings of the traditional AP algorithm using equal weight. Finally we apply the proposed method to group behavior analysis.
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This work is supported by the Special Funds of Basic Research Business Expenses of Central University under Grant No. JUSRP51510.
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Li, X., Zhou, Z., Liu, L. (2017). A Method of Group Behavior Analysis for Enhanced Affinity Propagation. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_43
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DOI: https://doi.org/10.1007/978-3-319-68542-7_43
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