Dissimilarity criteria in hierarchical clustering for interval-valued functional data
Nobuo Shimizu · International Journal of Knowledge Engineering and Soft Data Paradigms · 2011
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 interval-valued functional data can be considered to be a kind of symbolic data. We propose some new dissimilarity criteria in hierarchical clustering for interval-valued functional data as the extension of functional clustering method, and apply these criteria to real data.