FCM is a clustering method that allows each data point to belong to multiple clusters with varying degrees of membership. To configure clustering options, create an fcmOptions object. The FCM algorithm computes cluster centers and membership values to minimize the following objective function. Jm=C∑...
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plot(U(3,:),'-b+'); ylabel('Membership degrees') legend('FCM1','FCM2','FCM3','location','northeast'); toc 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35....
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FCM assigns each feature vector to one or more clusters based on their degrees of membership and its output encompasses cluster centers and membership values, indicating the degree of association for each data point with each cluster. Hence, FCM improves interpretability by distinctly delineating ...
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The concept of degrees of mem- bership, a cornerstone of fuzzy set theory, provides a more nuanced and flexible approach to categorization, making it a valuable addition to the machine learning toolkit for handling real-world problems characterized by impreci- sion, noise, and ambiguity. Among ...
FCM is a classical unsupervised fuzzy clustering algorithm [34,35], which divides the data into c number categories according to the membership degrees of the clustered data for solving optimization problems. Given a dataset X = {x1, x2,…, xn} with the c number of different clusters, FCM...
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