Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70169
Title: Uncertain data mining : an example in clustering location data
Authors: Chau, M
Cheng, R
Kao, B
Ng, J
Issue Date: 2006
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2006, v. 3918, p. 199-204 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UK-means to the particular pattern of moving-object uncertainty. Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results.
Description: The 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), Singapore, April 9-12, 2006
URI: http://hdl.handle.net/10397/70169
ISBN: 978-3-540-33206-0
978-3-540-33207-7
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/11731139_24
Appears in Collections:Conference Paper

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