Abstract
Although some clustering techniques are well known and widely used, their theoretical foundations are still unclear. We consider an approach, approximation clustering, as a unifying framework for making theoretical foundations to some popular techniques. The questions of interrelation of the models with each other and with some other methods (especially in contingency and spatial data analyses) are also discussed.
The research was supported by the Office of Naval Research under grants number N00014-93-1-0222 and N00014-96-1-0208 to Rutgers University.
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Mirkin, B. (1998). Mathematical Classification and Clustering: From How to What and Why. In: Balderjahn, I., Mathar, R., Schader, M. (eds) Classification, Data Analysis, and Data Highways. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72087-1_20
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DOI: https://doi.org/10.1007/978-3-642-72087-1_20
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