Difference between k means and c means

Question

I m trying to implement the k-means algorithm so i need to know what is the difference in the algorithm for k means and c means?
Are they same with different names or is there any difference?

Answer 1

Check out the answer given by @Reed Copsey in one of the Stackoverflow question. You will surely find your response:-

The main difference is that, in Fuzzy-C Means clustering, each point has a weighting associated with a particular cluster, so a point doesn't sit "in a cluster" as much as has a weak or strong association to the cluster, which is determined by the inverse distance to the center of the cluster.

Fuzzy-C means will tend to run slower than K means, since it's actually doing more work. Each point is evaluated with each cluster, and more operations are involved in each evaluation. K-Means just needs to do a distance calculation, whereas fuzzy c means needs to do a full inverse-distance weighting.