摘要
自组织特征映射作为一种神经网络方法,在数据挖掘、机器学习和模式分类中得到了广泛的应用。它将高维输入空间的数据映射到一个低维、规则的栅格上,从而可以利用可视化技术探测数据的固有特性。该文说明了自组织特征映射神经网络的工作原理和具体实现算法,同时利用一个算例展示了利用自组织特征映射进行聚类时的可视化特性,包括聚类过程的可视化和聚类结果的可视化,这也是自组织特征映射得到广泛应用的原因之一。
As a method of neural network, the self- organizing map（SOM） is an excellent tool for data mining, machine learning and pattern classification. It projects input spaee on prototypes of a low - dimensional regular grid that can be effectively utilized to visualize and explore properties of data. In this paper, the theory and algorithm of the SOM are considered. In particular, the visualization of clustering based SOM - the process visualization and the result visualization - is illuminated by an example. This also is one of the reason why SOM is used extensively.
出处
《计算机仿真》
CSCD
2006年第1期180-183,共4页
Computer Simulation
关键词
聚类
自组织特征映射
神经网络
可视化
Clustering
Self - organizing feature map
Neural network
Visualization
