Classification techniques include support vector machines, naive bayes classifiers, and logistic regression. The k-means clustering algorithm, the Gaussian (EM) clustering algorithm, and others are instances of clustering.
Two methods of pattern recognition used in machine learning are classification and clustering. Although there are some parallels between the two processes, clustering discovers similarities between things and groups them according to those features that set them apart from other groups of objects, whereas classification employs predetermined classes to which objects are assigned. "Clusters" are the name for these collections.
Clustering is framed in unsupervised learning in the context of machine learning, a branch of artificial intelligence. For this kind of algorithm, we only have one set of unlabeled input data, about which we must acquire knowledge without knowing what the outcome will be.
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