Abstract: Clustering algorithm is widely used in pattern recognition,information retrieval,image processing and natural language processing.Two common clustering methods based on neural network are GCS and SOM.Many scholars have proposed different improved algorithms based on them.GHTSOM(Growing Hierarchical Tree SOM) is one of them.GHTSOM works well for applications where there is a clear classification of data,but it is not suitable for applications where there is a lot of noise or disturbing data.The corrosion algorithm in image processing is used to optimize the GHTSOM algorithm,that is,before calling the GHTSOM process,the data is processed by the corrosion algorithm to remove the interference data or noise data at the junction of different classes of data,making a distinction between different categories of data more obvious.To make the presentation more intuitive,two-dimensional datas are used.The results show that the optimized Ghsom model can effectively avoid the unclassifiable problems caused by local connections between classes and the misclassified problems caused by too many neurons.

Key words: Clustering, Data corruption, Data processing, GHTSOM, Optimize