Basic concepts

k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster

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task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters)

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automated recognition of patterns and regularities in data

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the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data

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Dimensionality reduction, statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components

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condense the information of a large set of correlated variables into a few variables

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Unsupervised Learning