Author： Shimizu Nobuo
Publisher： Inderscience Publishers
ISSN： 1755-3210
Source： International Journal of Knowledge Engineering and Soft Data Paradigms, Vol.3, Iss.2, 2011-03, pp. : 132-142
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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.
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