In the field of artificial intelligence and machine learning, clustering refers to an unsupervised data analysis technique in which a set of objects or data is divided into groups or clusters according to their similarity. The goal of clustering is to find patterns in the data and group them in such a way that objects within a cluster are similar to each other and different from objects in other clusters.
Clustering algorithms can be used for different purposes, such as customer segmentation in marketing, identification of groups of patients with similar characteristics in medicine, document classification in natural language processing, among others. The most common clustering methods are k-means, hierarchical clustering and density-based clustering.
It is important to note that clustering is an exploratory technique and the optimal number of clusters to be formed is not known in advance, so additional analysis is required to assess the quality of the clusters and select the best model.
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