Overlapping Clustering

Overlapping clustering allows data points to belong to multiple clusters simultaneously. Unlike traditional "hard" clustering where each point is assigned to only one cluster, overlapping clustering acknowledges that data points can exhibit characteristics of several groups. This is particularly useful when dealing with complex datasets where boundaries between clusters are not well-defined. One algorithm that implements overlapping clustering is the Fuzzy C-Means (FCM) algorithm. FCM assigns a membership degree to each data point for each cluster, representing the probability of belonging to that cluster. A data point can have non-zero membership degrees for multiple clusters, indicating its partial membership in each.

Visit the following resources to learn more:

• @article@Unsupervised Clustering: A Guide