Title :
A density based membership function for fuzzy clustering
Author :
Acciani, G. ; Caradonna, R. ; Chiarantoni, E. ; Grassi, G.
Author_Institution :
Dipt. di Elettrotecnica ed Elettronica, Politecnico di Bari, Italy
Abstract :
This paper presents a new approach to fuzzy clustering using a membership function sensitive to density. It is a fuzzy membership function which allows the action range of the neural units matching the area they reach, even when the data set is contaminated by uniformly distributed noise points, without a need to fix a priori the number of clusters
Keywords :
fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); pattern recognition; sensitivity analysis; data set; fuzzy clustering; fuzzy neural nets; fuzzy set theory; learning process; membership function; sensitivity analysis; Clustering algorithms; Density functional theory; Fuzzy neural networks; Fuzzy sets; Measurement units; Neurons; Partitioning algorithms; Pollution measurement; Training data;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831118
