IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing map clustering analysis for molecular data
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
An improved method of feature selection based on concept attributes in text classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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The feature selection is an important part in automatic classification. In this paper, we use the HowNet to extract the concept attributes, and propose CHI-MCOR method to build a feature set. This method not only selects the highly occurring words, but also selects the word whose occurrence frequency is middle or low occurring words that are important for text classification. The combined method is much better than any one of the weight methods. Then we use the Self-Organizing Map (SOM) to realize automatic text clustering. The experiment result shows that if we can extract the sememes properly, we can not only reduce the feature dimension but also improve the classification precise. SOM can be used in text clustering in large scales and the clustering results are good when the concept feature is selected.