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What are K-Means Clustering?
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (center of cluster), serving as a prototype of the cluster. k-means clustering minimizes within-cluster variances, but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k- medians and k-medoids.
Clustering (or cluster analysis) is a method for identifying groups of things that are more closely related to one another than to objects in other groupings. Clustering can be used to group papers, music, and movies by distinct subjects, or it can be used to locate clients who have similar interests based on common buying histories as a basis for recommendation engines.
Objective Of clustering
Identify the structures and patterns in high dimensional data
Grouping data with similar pattern together
Partitioning the data into a predefined number of cluster k
Method of Clustering : - Alternatingly update
the cluster assignment of each data vector;
the cluster centroids.
Some Example of Clustering
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