Minimum hypervolume clustering algorithm

• Autorzy: Łęski J. , Henzel N.
• Języki publikacji: EN

Abstrakty

EN

The Hard C-Means (HCM) clustering method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greates disadvantage of this method is that the performance of the HCM is good only when the data set contains clusters that have approximately the same size and shape. The paper is devoted to a new clustering algorithm, called minimum hypervolume clustering (MHC), that seeks C hyperellipsoids with the smallest hypervolumes that enclose all the data points. Performances of the new clustering algorithm are experimentally verified using synthetic and real life data containing clusters with different size and orientations.

Słowa kluczowe

EN

c--means clustering; HCM; isodata algorithm

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2002
• Tom: Vol. 11, No. 1
• Strony: 123--132
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Łęski J.

• Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

autor

Henzel N.

• Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

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