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Group outlying aspects mining
conference contribution
posted on 2018-01-01, 00:00 authored by S Wang, H Xia, Gang LiGang Li, J Tan© 2018, Springer Nature Switzerland AG. 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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Event
Knowledge Science, Engineering and Management. International Conference (2018 : Changchun, China)Volume
11061Series
Lecture Notes in Computer SciencePagination
200 - 212Publisher
SpringerLocation
Changchun, ChinaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-08-17End date
2018-08-19ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319993645Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2018, Springer Nature Switzerland AGEditor/Contributor(s)
W Liu, F Giunchiglia, B YangTitle of proceedings
KSEM 2018: Knowledge Science, Engineering and ManagementUsage metrics
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