In this chapter, we presented some of the most common soft clustering approaches, focusing on their properties and features. Fuzzy c-means is an extension of the classic k-means algorithm, based on the concept of a fuzzy set. A cluster is not considered a mutually exclusive partition, but rather a flexible set that can overlap some of the other clusters. All of the samples are always assigned to all of the clusters, but a weight vector determines the membership level with respect to each of them. Contiguous clusters can define partially overlapped properties; hence, a given sample can have a not-null weight for two or more clusters. The magnitude determines how much it belongs to every segment.
Gaussian mixture is a generative process that is based on the assumption that it's possible to approximate a real data-generating process with a weighted sum of Gaussian distributions...
