Self Organizing maps Vs k-means

Does anyone know how well does Self Organizing Maps(SOM) compare to k-means? I believe usually in the color开发者_C百科 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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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.

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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.

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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.