| Publisher | University of Hong Kong | ||
|---|---|---|---|
| Format | 138.2KB PDF | Date added | 05 Jan 2006 |
| Topics | Data Mining - Analysis | ||
| Downloads | 40 | ||
Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement.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. The paper presents UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. The paper applies 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.
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