NDLI: On similarity measures for cluster analysis in clinical laboratory examination databases

Content Provider	IEEE Xplore Digital Library
Author	Hirano, S. Xiaoguang Sun Tsumoto, S.
Copyright Year	2002
Description	Author affiliation: Dept. of Med. Informatics, Shimane Med. Univ., Izumo, Japan (Hirano, S.; Xiaoguang Sun; Tsumoto, S.)
Abstract	This paper discusses how the conventional similarity measure works on the practical medical data set. The similarity measure used was linear combination of the Mahalanobis distance between numerical attributes and the Hamming distance between nominal attributes. We performed clustering experiments on the meningoencephalitis data set using the similarity measure in conjunction with four types of clustering algorithms: single- and complete-linkage agglomerative hierarchical clustering, Ward's method and rough clustering. Usefulness of the similarity measure was evaluated from the following viewpoints: (1) quality of the generated clusters; and (2) clinical reasonability of the attributes used to generate the high-quality clusters. The results show that the best clusters were obtained using Ward's method where the clinically reasonable attributes were selected. It suggests that this similarity measures would be applicable to the medical data sets.
Starting Page	1170
Ending Page	1175
File Size	851059
Page Count	6
File Format	PDF
ISBN	0769517277
ISSN	07303157
DOI	10.1109/CMPSAC.2002.1045170
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-26
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laboratories Databases Clustering methods Hamming distance Microorganisms Sun Biomedical informatics Performance evaluation Clustering algorithms Measurement standards
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Software

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