In the present paper a new fuzzy clustering algorithm is presented. It is a modified version of the Min-Max technique. By relying on the Principal Component Analysis, it overcomes some undesired properties of the original Simpson’s algorithm. In particular, a local rotation matrix is introduced for each hyperbox according to the data subset of the related cluster, so that it is possible to arrange the hyperbox orientation along any direction of the data space. Consequently, the new algorithm yields more efficient networks, improving the match between the resulting clusters and local data structure.
Clustering with Unconstrained Hyperboxes / FRATTALE MASCIOLI, Fabio Massimo; Rizzi, Antonello; Panella, Massimo; Martinelli, Giuseppe. - STAMPA. - 2:(1999), pp. 1075-1080. (Intervento presentato al convegno IEEE International Fuzzy Systems Conference tenutosi a Seul, Corea nel 22-25 agosto 1999) [10.1109/FUZZY.1999.793103].
Clustering with Unconstrained Hyperboxes
FRATTALE MASCIOLI, Fabio Massimo;RIZZI, Antonello;PANELLA, Massimo;MARTINELLI, Giuseppe
1999
Abstract
In the present paper a new fuzzy clustering algorithm is presented. It is a modified version of the Min-Max technique. By relying on the Principal Component Analysis, it overcomes some undesired properties of the original Simpson’s algorithm. In particular, a local rotation matrix is introduced for each hyperbox according to the data subset of the related cluster, so that it is possible to arrange the hyperbox orientation along any direction of the data space. Consequently, the new algorithm yields more efficient networks, improving the match between the resulting clusters and local data structure.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
