Clustering analysis of competitive learning network for molecular data

Abstract

In this paper competitive learning cluster are used for molecular data of large size sets. The competitive learning network can cluster the input data, it only adapts to the node of winner, the winning node is more likely to win the competition again when a similar input is presented, thus similar inputs are clustered into same a class and dissimilar inputs are clustered into different classes. The experimental results show that the competitive learning network has a good clustering reproducible, indicates the effectiveness of clusters for molecular data, the conscience learning algorithm can effectively cancel the dead nodes when the output nodes increasing, the kinds of network indicates the effectiveness of clusters for molecular data of large size sets. © Springer-Verlag Berlin Heidelberg 2006.