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Proceedings Paper

Hierarchical clustering method for the analysis of large amounts of data
Author(s): Hidezaku Nishizawa; Takashi Obi; Masahiro Yamaguchi; Nagaaki Ohyama
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Paper Abstract

Due to the development of digital information system in medical field, a large amount of image or signal data obtained from health examination has been stored. Analyzing these data is expected to make it possible to formulate new diagnostic knowledge for health care. In this paper, we propose a classification method suitable for the analysis of a large amount of medical data, for the purpose of assisting medical doctors to analyze the data. Int he proposed method, image or signal data are treated as vectors and mapped into multi-dimensional space, then hierarchical clustering method is applied. To obtain optimal division of cluster, a statistical criterion is introduced, and a binary tree of clusters is constructed base don the criterion. From the results of experiment using generated data and ECG signal, it is confirmed that the data sets can be correctly classified by our proposed method.

Paper Details

Date Published: 11 November 1996
PDF: 8 pages
Proc. SPIE 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments, (11 November 1996); doi: 10.1117/12.258130
Show Author Affiliations
Hidezaku Nishizawa, Tokyo Institute of Technology (Japan)
Takashi Obi, Tokyo Institute of Technology (Japan)
Masahiro Yamaguchi, Tokyo Institute of Technology (Japan)
Nagaaki Ohyama, Tokyo Institute of Technology (Japan)


Published in SPIE Proceedings Vol. 2824:
Adaptive Computing: Mathematical and Physical Methods for Complex Environments
H. John Caulfield; Su-Shing Chen, Editor(s)

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