Publication Details
Title: A Survey of Fuzzy Clustering Algorithms for Pattern Recognition
Author: A. Baraldi and P. Blonda
Group: ICSI Technical Reports
Date: October 1998
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1998/tr-98-038.pdf
Overview:
Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where clustering systems can be compared on the basis of their learning strategies. In the first part of this work, the following issues are reviewed: relative (probabilistic) and absolute (possibilistic) fuzzy membership functions and their relationships to the Bayes rule, batch and on-line learning, growing and pruning networks, modular network architectures, topologically perfect mapping, ecological nets and neuro-fuzziness. From this discussion an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed as a unifying framework in the comparison of clustering systems. Moreover, a set of functional attributes is selected for use as dictionary entries in our comparison. In the second part of this paper, five clustering algorithms taken from the literature are reviewed and compared on the basis of the selected properties of interest. These network clustering models are: i) Self-Organizing Map (SOM); ii) Fuzzy Learning Vector Quantization (FLVQ); iii) Fuzzy Adaptive Resonance Theory (Fuzzy ART); iv) Growing Neural Gas (GNG); and v) Fully self-Organizing Simplified Adaptive Resonance Theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear paradoxical. Firstly, only FLVQ, Fuzzy ART and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG and FOSART are considered effective fuzzy clustering algorithms.
Bibliographic Information:
ICSI Technical Report TR-98-038
Bibliographic Reference:
A. Baraldi and P. Blonda. A Survey of Fuzzy Clustering Algorithms for Pattern Recognition. ICSI Technical Report TR-98-038, October 1998
Author: A. Baraldi and P. Blonda
Group: ICSI Technical Reports
Date: October 1998
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1998/tr-98-038.pdf
Overview:
Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where clustering systems can be compared on the basis of their learning strategies. In the first part of this work, the following issues are reviewed: relative (probabilistic) and absolute (possibilistic) fuzzy membership functions and their relationships to the Bayes rule, batch and on-line learning, growing and pruning networks, modular network architectures, topologically perfect mapping, ecological nets and neuro-fuzziness. From this discussion an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed as a unifying framework in the comparison of clustering systems. Moreover, a set of functional attributes is selected for use as dictionary entries in our comparison. In the second part of this paper, five clustering algorithms taken from the literature are reviewed and compared on the basis of the selected properties of interest. These network clustering models are: i) Self-Organizing Map (SOM); ii) Fuzzy Learning Vector Quantization (FLVQ); iii) Fuzzy Adaptive Resonance Theory (Fuzzy ART); iv) Growing Neural Gas (GNG); and v) Fully self-Organizing Simplified Adaptive Resonance Theory (FOSART). Although our theoretical comparison is fairly simple, it yields observations that may appear paradoxical. Firstly, only FLVQ, Fuzzy ART and FOSART exploit concepts derived from fuzzy set theory (e.g., relative and/or absolute fuzzy membership functions). Secondly, only SOM, FLVQ, GNG and FOSART employ soft competitive learning mechanisms, which are affected by asymptotic misbehaviors in the case of FLVQ, i.e., only SOM, GNG and FOSART are considered effective fuzzy clustering algorithms.
Bibliographic Information:
ICSI Technical Report TR-98-038
Bibliographic Reference:
A. Baraldi and P. Blonda. A Survey of Fuzzy Clustering Algorithms for Pattern Recognition. ICSI Technical Report TR-98-038, October 1998