K-Mean Clustering Unsupervised Machine Learning in Python | KGP Talkie | Crash Course

Clustering is an unsupervised learning technique. It is the task of grouping together a set of objects in a way that objects in the same cluster are more similar to each other than to objects in other clusters. The similarity is an amount that reflects the strength of the relationship between two data objects. Clustering is mainly used for exploratory data mining. It is used in many fields such as machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.

K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster while keeping the centroids as small as possible. The ‘means’ in the K-means refers to averaging of the data; that is, finding the centroid.

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