Abstract
The paper builds on the representation of units/clusters with a special type of symbolic objects that consist of distributions of variables. Two compatible clustering methods are developed: the leaders method, that reduces a large dataset to a smaller set of symbolic objects (clusters) on which a hierarchical clustering method is applied to reveal its internal structure. The proposed approach is illustrated on USDA Nutrient Database.
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Korenjak-Černe, S., Batagelj, V. (2002). Symbolic Data Analysis Approach to Clustering Large Datasets. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_35
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DOI: https://doi.org/10.1007/978-3-642-56181-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43691-1
Online ISBN: 978-3-642-56181-8
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