K-Means alternatives and performance

I've been reading about similarity measures and image feature extraction; most of the papers refer to k-means as a good uniform clustering technique and my question is, is there any alternativ开发者_JAVA技巧e to k-means clustering that performs better for an specific set?

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You may want to look at MeanShift clustering which has several advantages over K-Means:

1. Doesn't require a preset number of clusters
2. K-Means clusters converge to n-dimensional voronoi grid, MeanShift allows other cluster shapes

MeanShift is implemented in OpenCV in the form of CAMShift which is a MeanShift adaptation for tracking objects in a video sequence.

If you need more info, you can read this excellent paper about MeanShift and Computer Vision: Mean shift: A robust approach toward feature space analysis

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A simple first step, you could generalize k-means to EM. But there are tons of clustering methods available and the kind of clustering you need depends on your data (features) and the applications. In some cases, even your distances you use matters and so might have to do some sort of distance transformation, if it is not in the kind of space you want it to be in.