Abstract
Existing works on outlying aspects mining have been focused on detecting the outlying aspects of a single query object, rather than the outlying aspects of a group of objects. While in many application scenarios, methods that can effectively mine the outlying aspects of a query group are needed. To fill this research gap, this paper extends the outlying aspects mining to the group level, and formalizes the problem of group outlying aspect mining. The Earth Move Distance based algorithm GOAM is then proposed to automatically identify the outlying aspects of the query group. The experiment result shows the capability of the proposed algorithm in identifying the group outlying aspects effectively.
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Acknowledgement
This work was supported by the International Cooperation Project of Institute of Information Engineering, Chinese Academy of Sciences under Grant No. Y7Z0511101, and also supported by the practical training project of high-level talents cross training of Beijing colleges and universities (BUCEA-2016-28).
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Wang, S., Xia, H., Li, G., Tan, J. (2018). Group Outlying Aspects Mining. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_18
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DOI: https://doi.org/10.1007/978-3-319-99365-2_18
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