Clustering analysis of competitive learning network for molecular data

Authors:
Lin Wang;Minghu Jiang;Yinghua Lu;Frank Noe;Jeremy C. Smith
Affiliations:
School of Electronics Eng., Beijing Univ. of Post and Telecom., Beijing, China;Lab. of Computational Linguistics, School of Humanities and Social Sciences, Tsinghua University, Beijing, China;School of Electronics Eng., Beijing Univ. of Post and Telecom., Beijing, China;Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany;Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 2
Cited 1

Self-organizing map analysis consistent with neuroimaging for chinese noun, verb and class-ambiguous word

ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Competitive learning with floating-gate circuits

IEEE Transactions on Neural Networks

A RBF network for chinese text classification based on concept feature extraction

ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

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.