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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!
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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.
In this paper, two clustering techniques are proposed, which are the K-means algorithm and Self-Organizing Maps - as a more rapid mode to classify patients with vertebral column conditions.
From a practical standpoint, a major difference is that you specify in advance the number of clusters you want with k-means but not with SOM. After we determine the cluster using the k-means, then... how the classification rate of clustering process can be interpreted? hoping that there are examples of programs for my references...
Deetz M. K-Means Clustering of Self-Organizing Maps: An Empirical Study on the Information Content of Self-Classification of Hedge Fund Managers. International Journal of Management Science and Business Administration. 2019 Mar,5 (3):43-57. Deetz, Marcus.
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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