whats is the difference between “k means” and “fuzzy c means” objective functions?

Question

I am trying to see if the performance of both can be compared based on the objective functions they work on?

Answer 1

people has written technically and each answer is well written. But what I want to say is the same in layman language. K means clustering cluster the entire dataset into K number of cluster where a data should belong to only one cluster. Fuzzy c-means create k numbers of clusters and then assign each data to each cluster, but their will be a factor which will define how strongly the data belongs to that cluster.