Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure Ting, Kai Ming, Zhu, Ye, Carman, Mark, Zhu, Ye and Zhou, Zhi-Hua 2016, Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure, in KDD 2016 : Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, [San Francisco, Calif.], pp. 1205-1214, doi: 10.1145/2939672.2939779. Attached Files Name Description MIMEType Size Downloads Title Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure Author(s) Ting, Kai Ming Zhu, Ye Carman, Mark Zhu, Ye Zhou, Zhi-Hua Conference name Knowledge Discovery and Data Mining. Conference (2016 : 22nd : San Francisco, California) Conference location San Francisco, California Conference dates 2016/08/13 - 2016/08/17 Title of proceedings KDD 2016 : Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Publication date 2016 Start page 1205 End page 1214 Total pages 10 Publisher ACM Place of publication [San Francisco, Calif.] Keyword(s) Data dependent dissimilarity distance measure distance-based neighbourhood probability-mass-based neighbourhood k nearest neighbours ISBN 9781450342322 Language eng DOI 10.1145/2939672.2939779 HERDC Research category E1.1 Full written paper - refereed ERA Research output type E Conference publication Persistent URL http://hdl.handle.net/10536/DRO/DU:30097544 Document type: Conference Paper Collection: School of Information Technology Related Links Link Description   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 05 Jul 2017, 15:36:47 EST Thu, 06 Jul 2017, 05:00:56 EST Fri, 17 Nov 2017, 05:01:35 EST Mon, 14 May 2018, 12:14:30 EST Mon, 14 May 2018, 12:17:34 EST Mon, 14 May 2018, 12:18:28 EST Wed, 23 May 2018, 12:47:14 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 21 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 05 Jul 2017, 15:36:47 EST

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