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Alex Rivera
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I have a large data set that I would like to cluster. My trial run set size is 2,500 objects; when I run it on the 'real deal' I will need to handle at least 20k objects. These objects have a cosine similarity between them. This cosine similarity does not satisfy the requirements of being a mathematical distance metric; it doesn't satisfy the triangle inequality. I would like to cluster them in some "natural" way that puts similar objects together without needing to specify beforehand the number of clusters I expect. Does anyone know of an algorithm that would do that? Really, I'm just looking for any algorithm that doesn't require a) a distance metric and b) a pre-specified number of clusters. Many thanks! This question has been asked before here: Clustering from the cosine similarity values (but this solution only offers K-means clustering), and here: Effective clustering of a similarity matrix (but this solution was rather vague)
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