Exclusive Clustering

A form of grouping that stipulates a data point can exist only in one cluster. This can also be referred to as “hard” clustering. The K-means clustering algorithm is an example of exclusive clustering.

A common example of an exclusive clustering method: K-means clustering

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Method¶

Hard k-means is the standard, traditional version of the k-means algorithm.
It assigns each data point to exactly one cluster, based on the Euclidean distance between the data point and the cluster centers.
The basic idea of k-means is to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. OR
Data points are assigned into K groups, where K represents the number of clusters based on the distance from each group’s centroid.
The data points closest to a given centroid will be clustered under the same category.
- A larger K value will be indicative of smaller groupings with more granularity
- A smaller K value will have larger groupings and less granularity.

Application¶

Market segmentation
Document clustering
Image segmentation
Image compression