An Automatic Clustering Algorithm and Its Properties in High-Dimensional Spaces

An economical technique for approximating a joint N-dimensional probability density function has been described by Sebestyen and Edie [20]. The algorithm searches for clusters of points and considers each cluster as one hyperellipsoidal cell in an N-dimensional histogram. Among the advantages of thi...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics man, and cybernetics, 1972-04, Vol.SMC-2 (2), p.247-254
Hauptverfasser:	Mucciardi, Anthony N., Gose, Earl E.
Format:	Artikel
Sprache:	eng
Schlagworte:	Automatic control Clustering algorithms Decision making Error analysis Extrapolation Histograms Probability density function Robustness Shape control Testing
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