Title: Dissimilarity criteria in hierarchical clustering for interval-valued functional data

Authors: Nobuo Shimizu

Addresses: The Institute of Statistical Mathematics, 10-3, Midoricho, Tachikawa-shi, Tokyo 190-8562, Japan

Abstract: We deal with hierarchical clustering for interval-valued functional data. Functional data is defined as the data which is function, or as the data approximated as a function. Functional clustering is proposed as clustering for functional data. Interval-valued functional data is defined as the functional data whose range corresponding to each value in the domain is interval-valued data. Interval-valued data is especially typical in symbolic data, and also intervalvalued functional data can be considered to be a kind of symbolic data. We propose some new dissimilarity criteria in hierarchical clustering for intervalvalued functional data as the extension of functional clustering method, and apply these criteria to real data.

Keywords: functional data analysis; FDA; symbolic data analysis; SDA; dissimilarity criteria; hierarchical clustering.

DOI: 10.1504/IJKESDP.2011.045725

International Journal of Knowledge Engineering and Soft Data Paradigms, 2011 Vol.3 No.2, pp.132 - 142

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 24 Feb 2012 *

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