# Clustering a set of vectors

Provided a set ($$m$$ no. of) of n-dimensional vectors what would be the correct unsupervised approach to cluster them? The vectors essentially represent patterns.

For example: Set of vector is represented as $$V$$. Let a vector $$v1$$ represents a pattern similar to $$y = sin(x)$$ curve. The $$y$$ values are stored in $$v1$$ and the $$x$$ intervals are same for all the vectors. Similarly there is a vector $$v2$$ representing pattern similar to $$y=log(x)$$.

The problem: Does a group of vectors exist, exhibiting similar (not exactly same) pattern as $$v1$$, similarly for $$v2$$ and so on?

Therefore these patterns are required to be clustered appropriately. There are methods such as Vector Quantization, but I am not sure if those methods are appropriate in this case.