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I have a clustering problem which I can't seem to solve, although if I treat it as a labeled classification problem, I can solve it with satisfactory precision. Is there an elegant way to make the leap from being able to solve the classification problem, to being able to solve the clustering one?

The details:

I have a labeled dataset of movements, from which I extract a features vector for each movement. I can then perform classification of the movements with several tools (SVM, decision trees, etc.) and I get satisfactory results (0.85 precision, which in my case is ok).

The problem is, that the real world data I am facing, will of course not be labeled, and is not modeled after the labeled data I have (which means I can't train a classifier on the labeled data, and expect it to perform well on other data). The real world data will for sure not be comprised just of these two classes, it will be comprised of K unknown classes, which I would like to be able to cluster with satisfactory precision.

So is it logical to assume that if I can solve the classification problem with high precision, I will be able to solve the clustering problem with high precision? Does it mean that my feature extraction, which works well for the classification, will work well for clustering?

Because just throwing clustering algorithms at the problem (k-means, meanshift, dbscan, etc.) doesn't give good results. Usually I just get too many clusters, or just 1 cluster (although I know I have 2 classes), with horrible precision.

Btw I am using Python's sklearn.

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If you have only one or two feature variables, I would hesitate to recommend clustering methods, because you may even not know there will be clusters or how many clusters. I would rather try kernel density estimation on the data to observe the peaks of probability density. And it is not difficult to write a python code with SciPy package (the example code is shown on that page too).

If you have much more features, which makes your problem more complicated, you can try clustering methods. And you can estimate the number of clusters from the data by "one-added-at-a-time" and "split-the-cluster" strategy (called xmeans), or brute-force on k to determine the optimal number of cluster based on sum-of-squares. Refer to this post with similar topic.

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You need to use a customized clustering approach that integrates your requirements and domain knowledge.

DBSCAN is quite flexible, at least if you have a good implementation. But for your problem at hand you really need to define what "similar" means. Euclidean distance won't do the trick, and when Euclidean distance doesn't work, no clustering using euclidean distance will work. So first make sure, you have the appropriate similarity measure (and this may mean implementing one yourself!)

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