Best Clustering Algorithm for High Dimensional Vectors
45 dimensions is not particularly high. It's at best "medium" dimensionality, so most algorithms could work.
Usually it's not so much a matter of the number of dimensions, but rather how well they are preprocessed. With bad preprocessing, 2 dimensions can be a problem if the signal in one attribute is drowned by the noise in the other.
There is no automatic way to get this right, otherwise it would be in all the libraries. Scaling can help, but can also harm. It's up to the user to prepare the data and choose parameters (such as distance functions and algorithms) to achieve the desired effect, because there is no computable equation fot "desirable".
I am attempting to use some sort of clustering method on a set of datapoint vectors which have 45 dimensions. I'm fairly new to clustering data points and was wondering if anyone could point out appropriate methods to utilize? I was attempted using K-Means Clustering but was wondering if the dimensionality of my data might be too large for this?