A fast algorithm for finding correlation clusters in noise data
Paper
Paper/Presentation Title | A fast algorithm for finding correlation clusters in noise data |
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Presentation Type | Paper |
Authors | Li, Jiuyong (Author), Huang, Xiaodi (Author), Selke, Clinton (Author) and Yong, Jianming (Author) |
Editors | Zhou, Zhi-Hua, Li, Hang and Yang, Qiang |
Journal or Proceedings Title | Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007) |
Journal Citation | 4426, pp. 639-647 |
Number of Pages | 9 |
Year | 2007 |
Publisher | Springer |
Place of Publication | Germany |
ISBN | 9783540717003 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-540-71701-0_68 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-540-71701-0_68 |
Conference/Event | 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007) |
Event Details | 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007) Parent Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Delivery In person Event Date 22 to end of 25 May 2007 Event Location Nanjing, China |
Abstract | Noise significantly affects cluster quality. Conventional clustering methods hardly detect clusters in a data set containing a large amount of noise. Projected clustering sheds light on identifying correlation clusters in such a data set. In order to exclude noise points which are usually scattered in a subspace, data points are projected to form dense areas in the subspace that are regarded as correlation |
Keywords | generalised projected clustering; SVD decomposition |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Series | Lecture Notes in Computer Science (Book series) |
Byline Affiliations | Department of Mathematics and Computing |
University of New South Wales | |
School of Information Systems |
https://research.usq.edu.au/item/9y486/a-fast-algorithm-for-finding-correlation-clusters-in-noise-data
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