**Clustering Algorithms a brief
overview
K-means Algorithm
K-means clustering is a method of cluster analysis which aims to partition
**n observations into k clusters in which
each observation belongs to the cluster with the nearest mean. K-means
clustering can be described as a partitioning method. Unlike the hierarchical
clustering methods, k-means does not create a tree structure to describe the
groupings in your data, but rather creates a single level of clusters. K-means
uses an iterative algorithm that minimizes the sum of distances from each object
to its cluster centroid, over all clusters. This algorithm moves objects between
clusters until the sum cannot be decreased further. The result is a set of
clusters that are as compact and well-separated as possible.

Hierarchical Clustering Algorithm

to obtain the similarity, in this case a distance matrix. From this point of view, hierarchical clustering is the ideal method of clustering, but has not been preferred much due to other complexities.

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