Hierarchical clustering method for the analysis of large amounts of data

Hidezaku Nishizawa; Takashi Obi; Masahiro Yamaguchi; Nagaaki Ohyama

doi:10.1117/12.258130

11 November 1996 Hierarchical clustering method for the analysis of large amounts of data

Hidezaku Nishizawa, Takashi Obi, Masahiro Yamaguchi, Nagaaki Ohyama

Proceedings Volume 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments; (1996) https://doi.org/10.1117/12.258130
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

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.

Citation Download Citation

Hidezaku Nishizawa, Takashi Obi, Masahiro Yamaguchi, and Nagaaki Ohyama "Hierarchical clustering method for the analysis of large amounts of data", Proc. SPIE 2824, Adaptive Computing: Mathematical and Physical Methods for Complex Environments, (11 November 1996); https://doi.org/10.1117/12.258130

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