数据腐蚀对GHTSOM模型的优化

Optimization of GHTSOM Model by Data Corrosion

聚类算法被广泛应用于模式识别、信息检索、图像处理,以及自然语言处理等领域,GCS和SOM是两种常用的基于神经网络思想的聚类方式,很多学者在它们的基础上提出了不同的改进算法,GHTSOM(Growing Hierarchical Tree SOM)便是其中之一,对于数据分类较为清晰的应用场景效果良好,但不适用于干扰数据或者噪声数据较多的应用场景。利用图像处理中的腐蚀算法对GHTSOM算法进行优化,即在调用GHTSOM过程之前,先用腐蚀算法对数据进行处理,去除掉不同类别的数据交界位置处的干扰数据或者噪声数据,使不同类别数据之间出现较为明显的界限。为使表达更加直观,采用二维数据进行处理分析,结果表明,优化后的GHTSOM模型可有效避免由于类间局部连接造成的无法分类的问题,以及由于神经元过多所造成的误分类问题。

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.