Abstract
Evaluating distances between uncertain objects is needed for some uncertain data mining techniques based on distance. An uncertain object can be described by uncertain numerical or categorical attributes. However, many uncertain data mining algorithms mainly discuss methods of evaluating distances between uncertain numerical objects. In this paper, an efficient method of evaluating distances between uncertain categorical objects is presented. The method is used in nearest-neighbor classifying. Experiments with datasets based on UCI datasets and the plant dataset of “Three Parallel Rivers of Yunnan Protected Areas” verify the method is efficient.
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Chen, H., Wang, L., Liu, W., Xiao, Q. (2010). Evaluating the Distance between Two Uncertain Categorical Objects. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_12
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DOI: https://doi.org/10.1007/978-3-642-17313-4_12
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