Evaluating the distance between two uncertain categorical objects

Authors:
Hongmei Chen;Lizhen Wang;Weiyi Liu;Qing Xiao
Affiliations:
Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China;Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Year:
2010

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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.