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Categories based on cluster model
      • Hierarchical clustering
      • Centroid-based clustering
      • Distribution-based clustering
      • Density-based clustering

The most appropriate clustering algorithm for a particular problem often needs to be chosen experimentally.

Hierarchical clustering

Objects being more related to nearby objects than to objects farther away.

Centroid-based clustering (k-means clustering)

Find the k cluster centers and assign the objects to the nearest
cluster center, such that the squared distances from the cluster
are minimized.

Distribution-based clustering

Objects of a cluster belong most likely to the same distribution (e.g., Gaussian distributions).

Density-based clustering

Clusters are defined as areas of higher density than the  remainder of the data set.