How can I use fuzzy clustering FCM to deal with incomplete or missing data ? I would be very grateful for answer with this simple data set, if we randomly remove amount of data from test set and use them as missing data.
i.e data set:
d=[0.3 0.4 0.3
0.4 0.5 0.4
0.4 0.3 0.4
0.5 0.7 0.5
0.5 0.5 0.5
0.6 0.6 0.6
0.7 0.8 0.7
0.7 0.6 0.7
0.8 0.7 0.8
];
f=[1;1;1;2;2;2;3;3;3];
The question doesn't seem right. FCM clusters with whatever data it has in hand. It doesn't work as multi-fold cross validation.
your topic seems to be classification where cross-validation is used for evaluation of the model used, and not clustering. BTW, why you want to use a part of the test data as missing data (and not as training data?).
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