Speeding up fuzzy clustering with neural network techniques
Christian Borgelt, Rudolf Kruse · 2004
We explore how techniques that were developed to improve the training process of artificial neural networks can be used to speed up fuzzy clustering. The basic idea of our approach is to regard the difference between two consecutive steps of the alternating optimization scheme of fuzzy clustering as providing a gradient, which may be modified in the same way as the gradient of neural network back-propagation is modified in order to improve training. Our experimental results show that some methods actually lead to a considerable acceleration of the clustering process.