When you need to clusterize data, fuzzy c-means is an appealing candidate, being it more robust and stable than the k-means clustering algorithm. This implementation is faster than that found in the Fuzzy Logic Toolbox (fcm.m). In addition, you can run it without having to buy the FL Toolbox. With this entry I want to stimulate the involvment of other users, to further speedup it and with the ultimate goal to eventually find the TRUE fastest fcm in the West!!
Marco Cococcioni (2021). ffcmw: The Fastest Fuzzy C-Means in the West! (https://www.mathworks.com/matlabcentral/fileexchange/53029-ffcmw-the-fastest-fuzzy-c-means-in-the-west), MATLAB Central File Exchange. Retrieved .
How can I use weights for each data point?
thank you so much, please page number that explains the algorithm
Many people are asking me a reference to my fuzzy c-means implementation.
This implementation is aligned with the description provided in the following book:
Bezdec, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
In addition, my implementation perform the same computations done by Matlab implementation by function fcm.m.
The only difference with fcm.m is that my version heavily uses Matlab vectorized functions. Only on for loop is used (the outher one).
This is way it is faster.
Hello. It works well. If you don't mind, can I get a paper or literature that describes this algorithm?
Hi Tan Yuki, I guess it can. But I need to better understood your problem.
Please drop me an email privately.
You can find my email address here:
Hi. it really work very fast. Can this be implement on interest object's image rather than sample points?
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