Fuzzy K-means clustering is a fuzzy form of the K-means algorithm that we discussed earlier. Fuzzy K-means generate soft clusters, in which a point can belong to more than one cluster and will have a valid affinity in that cluster. Fuzzy K-means is helpful in defining the relationship between the different data points. It is good for clustering text documents. This algorithm takes similar parameters as K-means, except for one additional fuzziness factor. Fuzzy K-means iterates over the dataset and calculates a degree of association of each point to each of the clusters. So, the degree of association happens in this way—suppose that we have centroids c₁, c₂, and c₃, and we have distances d1, d2, and d3 for a vector V from centroids c₁, c₂, and c₃.

The degree of association for centroid c1 will be calculated as:

Here, k is the number of centroids and m is the fuzziness factor. In the same way, we can calculate the degree of association with other centroids after...