I have a large set of multi dimensional data.The data points are highly skewed and not smoothly distributed.I want to divide the data set to some finite number of bins.I have approached this problem with some clustering algorithms(like KMeans and Mean shift).But as there are many clustering algorithms I am not sure which one would be most efficient. Again I can apply clustering to multi dimensional data only.But how should I approach when the data set consists of single dimensional data(like a large 1-D array) only?
You should try other distance measures.
For example Canberra and Clark distances often work much better with skewed distributions. So they are worth a try.