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I have dataset of items and want to cluster them. However, I don't have a predefined distance function. Does it make sense to learn a classifier that can predict the similarity between any two items?

Dataset: A, B, C, ...

Unknown distance function: dfn(X, Y) => [0.0, 1.0]

Training dataset for classifier (let us say we only train for similar(1.0) or not(0.0)) A, B => 1.0 A, C => 0.0 A, D => 1.0 ...

After this, during the clustering operation, when I need a distance score for any two items x and y. Can I use classify(x, y) as the distance function?

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A classifier is usually not well designed to produce a meaningful distance. The value returned is a confidence but the reason for low confidence doesn't necessarily mean the objects are similar.

Instead, have a look at distance learning literature. People have spend effort into coming up with clever ways of learning distances from training data.

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  • $\begingroup$ Thanks for the answer. Would you be able to point me to a good starting point (A gentle introduction or Wikipedia article etc?). $\endgroup$
    – Prashanth Ellina
    Jan 15, 2015 at 16:23
  • $\begingroup$ I'm not using distance learning myself. $\endgroup$
    – Has QUIT--Anony-Mousse
    Jan 15, 2015 at 17:36
  • $\begingroup$ Other names for distance learning are: metric learning and similarity learning Both names in google will give you some of the relevant literature $\endgroup$
    – Jacques Wainer
    Feb 24, 2015 at 11:07

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