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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!
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.