Abstract
In 1987, Diday added a new dimension to data analysis with his fundamental paper introducing the notions of symbolic data and their analyses. He and his colleagues, among others, have developed innumerable techniques to analyse symbolic data; yet even more is waiting to be done. One area that has seen much activity in recent years involves the search for a measure of dependence between two symbolic random variables. This paper presents a covariance function for interval-valued data. It also discusses how the total, between interval, and within interval variations relate; and in particular, this relationship shows that a covariance function based only on interval midpoints does not capture all the variations in the data. While important in its own right, the covariance function plays a central role in many multivariate methods.
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Billard, L. (2007). Dependencies and Variation Components of Symbolic Interval-Valued Data. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_1
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DOI: https://doi.org/10.1007/978-3-540-73560-1_1
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