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Clustering means organizing similar objects into groups within a machine-learning algorithm. Clustering has many uses in data science, like image processing, knowledge discovery in data, and unsupervised learning. Cluster analysis, or clustering, is done by scanning the unlabeled datasets in a machine-learning model and setting measurements for specific data point features. The cluster analysis will then classify and place the data points in a group with matching features. The clustering technique to break down large, intricate datasets in a machine-learning model can simplify complex data.

Clustering is a form of unsupervised learning vs. classification, which is supervised learning. In clustering, there are no training sets and no labels. Depending on which data characteristics are important, some of these points will be similar to other data points. These are clusters. They tell us that the pieces of data are similar based on the parameters set.

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