For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Generalized Fuzzy Kohonen Clustering Networks
Ching-Tang HSIEH Chieh-Ching CHIN Kuang-Ming SHEN
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 1998/10/25
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Information Theory and Its Applications)
Category: Neural Networks/Signal Processing/Information Storage
fuzzy sets, self-organization, clustering,
Full Text: PDF>>
A fuzzy Kohonen clustering network was proposed which integrates the Fuzzy c-means (FCM) model into the learning rate and updating strategies of the Kohonen network. This yields an optimization problem related to FCM, and the numerical results show improved convergence as well as reduced labeling error. However, the clusters may be either hyperspherical-shaped or hyperellipsoidal-shaped, we use a generalized objective function involving a collection of linear varieties. In this way the model is distributed and consists of a series of `local' linear-type models (based on the revealed clusters). We propose a method to generalize the fuzzy Kohonen clustering networks. Anderson's IRIS data and the artificial data set are used to illustrate this method; and results are compared with the standard Kohonen approach and the fuzzy Kohonen clustering networks.