A novel Matlab routine, called FKML0, is proposed to implement a variant of the standard Fuzzy K-Means algorithm. Such a variant allows for sparsity in the fuzzy membership degree matrix by adding a suitable regularization term based on the L0 norm to the Fuzzy K-Means loss function. The new clustering algorithm, called Fuzzy K-Means with L0 regularization, is able to assign, on the one hand, objects clearly belonging to a cluster with membership degrees equal to 1 (and 0 elsewhere) and, on the other hand, objects with unclear assignment with fuzzy membership degrees in the interval [0,1].
FKML0: A Matlab Routine for Sparse Fuzzy Clustering / Ferraro, Maria Brigida; Forti, Marco; Giordani, Paolo. - 8:(2024), pp. 1-8. (Intervento presentato al convegno 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) tenutosi a Yokohama, Japan) [10.1109/fuzz-ieee60900.2024.10611853].
FKML0: A Matlab Routine for Sparse Fuzzy Clustering
Ferraro, Maria Brigida;Forti, Marco;Giordani, Paolo
2024
Abstract
A novel Matlab routine, called FKML0, is proposed to implement a variant of the standard Fuzzy K-Means algorithm. Such a variant allows for sparsity in the fuzzy membership degree matrix by adding a suitable regularization term based on the L0 norm to the Fuzzy K-Means loss function. The new clustering algorithm, called Fuzzy K-Means with L0 regularization, is able to assign, on the one hand, objects clearly belonging to a cluster with membership degrees equal to 1 (and 0 elsewhere) and, on the other hand, objects with unclear assignment with fuzzy membership degrees in the interval [0,1].I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.