This algorithm works by assigning membership to each data point corresponding to each cluster center on the basis of the distance between the cluster center and the data point. The more the data is near to the cluster center more is its membership towards the particular cluster center. Clearly, the summation of membership of each data point should be equal to one.

After each iteration membership and cluster centers are updated according to the formula:

**Advantages**

- Gives the best result for the overlapped data set and comparatively better than k-means algorithm.
- Unlike k-means where data point must exclusively belong to one cluster center here data point is assigned membership to each cluster center as a result of which data point may belong to more than one cluster center.

**Disadvantages**

- With a lower value of β we get a better result but at the expense of more iteration.
- Euclidean distance measures can unequally weigh underlying factors.

**Reference**

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