What are the advantages and disadvantages of such an approach?
For the fuzzy c-means algorithm described in this book, the sum of the mem-
bership degree of any point over all clusters is 1. Instead, we could only
require that the membership degree of a point in a cluster be between 0 and
The main advantage of this approach occurs when a point is an outlier and
does not really belong very strongly to any cluster, since in that situation,
the point can have low membership in all clusters. However, this approach is
often harder to initialize properly and can perform poorly when the clusters
are not are not distinct. In that case, several cluster centers may merge
together, or a cluster center may vary significantly from one iteration to
another, instead of changing only slightly, as in ordinary K-means or fuzzy
c-means.
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What will be an ideal response?
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What will be an ideal response?