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.