L1 and L2 Approximation Clustering for Mixed Data: Scatter Decompositions and Algorithms
Clustering is considered usually an art rather than a science because of lacking comprehensive mathematical theories in the discipline. The major issue raised in this paper is that L2 and L1 approximation bilinear clustering can provide a theoretical framework for an extensive part of partitioning a...
Veröffentlicht in: | Lecture Notes-Monograph Series. - Institute of Mathematical Statistics, 1982. - 31(1997) vom: Jan., Seite 473-486 |
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1. Verfasser: | |
Format: | Online-Aufsatz |
Sprache: | English |
Veröffentlicht: |
1997
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Zugriff auf das übergeordnete Werk: | Lecture Notes-Monograph Series |
Schlagworte: | Partitioning Hierarchy Mixed data Approximation Contingency coefficients Mathematics Behavioral sciences Information science Applied sciences Philosophy |
Zusammenfassung: | Clustering is considered usually an art rather than a science because of lacking comprehensive mathematical theories in the discipline. The major issue raised in this paper is that L2 and L1 approximation bilinear clustering can provide a theoretical framework for an extensive part of partitioning and hierarchic clustering concerning its algorithmical and interpretational aspects, which is supported with a theoretical evidence. |
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ISSN: | 07492170 |