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Let's say I have a dataset, each item/row of which has $\mathit{X + 1}$ characteristics where the last characteristic (i.e., the $\mathit{1}$) represents the some value I want to predict, $\mathit{Y}$, based on a SOM trained on the $\mathit{X}$ characteristics. I want to organize the dataset into groups such that each group has a small variance among the respective $\mathit{Y}$ values. I believe I could do this by using a non-Euclidean distance to find the Best Matching Unit (BMU) based on applying weights to each dimension.

For example, given a node at (0,0) and weights for dimension $\mathit{x}$ of 1 and dimension $\mathit{y}$ of 2, a data point at (3,2) would have a weighted distance of 5 from the node, calculated as follows:

$\sqrt{\mathit{(1 * (3 - 0)) ^ 2 + (2 * (2 - 0)) ^ 2}}$

I don't think a simple linear regression would work to determine the weights because it would not take advantage of clustering.

The goal would be, for a new data point, to approximate a probability distribution of outcomes based on similarly-profiled data points in the training set (i.e., retrieve all of the training results with the same BMU and analyze the results). I think this might essentially just be replicating a deep feedforward network, but I'd like to try it.

Is there a way I could achieve this by modifying a SOM model or using a similar technique?

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