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Clustering is an unsupervised machine learning method that divides the data into different clusters and places them in separate classes. Unsupervised learning algorithms are those which don’t need any form of labelling on the input data, and there is no human to give feedback.
Such algorithms are used when patterns and insights need to be extracted from unstructured or semi-structured data which is unlabelled.
The process of clustering divides the input dataset which is fed to a clustering algorithm into different data points based on how similar these points are to one another. Points which are not similar to one another at all are placed in far off groups whereas similar points are placed in the same class or nearby class.
It helps in grouping data that is similar in certain aspects together, thereby labelling such data (indirectly). This way, similar data would lie in one class thereby making it easy to perform computations on this specific type of data.
Clustering algorithms: There are many clustering algorithms and the most widely used algorithm is k-means clustering. Other clustering algorithms include Mean-shift clustering, and Density based spatial clustering of applications with noise (DBSCAN).
It is one of the simplest and widely used algorithms since it is easy to implement.
In this post, we understood the meaning and significance of clustering, which is an unsupervised learning algorithm.