自组织映射与 k 均值
有谁知道自组织映射（SOM）与 k 均值相比效果如何？我相信通常在颜色空间（例如 RGB）中，SOM 是将颜色聚类在一起的更好方法，因为视觉上不同颜色之间的颜色空间存在重叠（http://www.ai-junkie.com/ann/som/som1.html）。 k-means 是否存在优于 SOM 的情况？
谢谢！
Does anyone know how well does Self Organizing Maps(SOM) compare to k-means? I believe usually in the color space,such as RGB, SOM is a better method to cluster colors together as there is overlap in the color space between visually different colors (http://www.ai-junkie.com/ann/som/som1.html). Are there cases where k-means outperforms SOM?
Thanks!
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我相信 K-means 是 SOM 的专业化。我确信您可以为其构建理想的案例。我认为计算速度是它的主要优势——当你逐步改进人工智能算法时，有时更差的算法的更多迭代会比更好、更慢的算法的更少迭代提供更好的性能。
这一切都取决于数据。在运行之前你永远不会知道。
K-means is a specialisation of SOM, I believe. You can construct ideal cases for it, I'm sure. I think computational speed is its major advantage -- when you have incrementally improving AI algorithms, sometimes more iterations of a worse algorithm gives better performance than fewer iterations of a bettwer, slower algorithm.
It all depends on the data. You never know until you run it.
K 均值是自组织映射 (SOM) 的子集。 K-means 严格来说是 n 空间邻居的平均 n 维向量。 SOM类似，但其思想是使候选向量更接近匹配向量，并通过扰动它们来增加与周围向量的差异；扰动随着距离的增加而减小（核宽度）；这就是名称中“自组织”部分的由来。
K-means is a subset of Self-Organizing Maps (SOM). K-means is strictly an average n-dimensional vector of the n-space neighbors. SOM is similar but the idea is to make a candidate vector closer to the matching vector and increase the difference with surrounding vectors by perturbing them; the perturbation decreases (kernel width) with distance; that is where the Self-Organizing part of the name comes from.
自组织映射创建二维输出。 k-means 是多维的。
SOM 以离散化表示（网格）运行。 SOM 使用更局部的规则（邻域函数）。
k-means 作为一种聚类算法得到了更广泛的应用。
Self Organizing Maps create a 2-dimensional output. k-means is multi-dimensional.
SOMs operate in a discretized representation (grid). SOMs use a more local rule (neighborhood function).
k-means is more widely used as a clustering algorithm.