SOM(自组织图)和K-Means有什么区别? [英] What is the difference between SOM (Self Organizing Maps) and K-Means?

问题描述

stackoverflow中只有一个与此相关的问题，更多的是哪个更好.我只是真的不明白区别.我的意思是，它们都使用向量，它们被随机分配给聚类，它们都使用不同聚类的质心来确定获胜的输出节点.我的意思是，区别到底在哪里?

There is only one question related to this in stackoverflow, and it is more about which one is better. I just dont really understand the difference. I mean they both work with vectors, which are assigned randomly to clusters, they both work with the centroids of the different clusters in order to determine the winning output node. I mean, where exactly lies the difference?

推荐答案

在K均值中，节点(质心)彼此独立.获胜的节点有机会适应每个自我，并且只有那样.在SOM中，将节点(质心)放置在网格上，因此每个节点都被视为具有一些邻居，这些邻居与节点相邻或接近于它们在网格上的位置.因此，获胜的节点不仅会自我适应，还会为其邻居造成变化.如果在修改质心向量时不考虑邻居，则可以将K均值视为SOM的特殊情况.要了解更多，您仍然可以在Google上搜索它....

In K-means the nodes (centroids) are independent from each other. The winning node gets the chance to adapt each self and only that. In SOM the nodes (centroids) are placed onto a grid and so each node is consider to have some neighbors, the nodes adjacent or near to it in repspect with their position on the grid. So the winning node not only adapts itself but causes a change for its neighbors also. K-Means can be considered a special case of SOM were no neighbors are taken into account when modifing centroids vectors. For more, you can still google it ....

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