计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 664-667.doi: 10.11896/jsjkx.200500129
石健1, 莫俊2
SHI Jian1, MO Jun2
摘要： 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。
中图分类号:
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