- Title
- Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure
- Creator
- Ting, Kaiming; Zhu, Ye; Carman, Mark; Zhu, Yue; Zhi-Hua, Zhou
- Date
- 2016
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161653
- Identifier
- vital:12527
- Identifier
-
https://doi.org/10.1145/2939672.2939779
- Identifier
- ISBN:9781450342322
- Abstract
- This paper introduces the first generic version of data dependent dissimilarity and shows that it provides a better closest match than distance measures for three existing algorithms in clustering, anomaly detection and multi-label classification. For each algorithm, we show that by simply replacing the distance measure with the data dependent dissimilarity measure, it overcomes a key weakness of the otherwise unchanged algorithm.
- Relation
- 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco; August 13th-17th, 2016 p. 1205-1214
- Rights
- Copyright ACM
- Rights
- This metadata is freely available under a CCO license
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