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gaussian_mixture_model icon gaussian_mixture_model

a brief summary of Gaussian mixture models. for more information about what this code does exactly ,please check the document file.

kmeans icon kmeans

propess of this code is learning some basic information about kmeans not just the function that we all probaby use but also definition of kmean and what it exactly does.

linkage icon linkage

The following linkage methods are used to compute the distance between two clusters s and d. i explained more about this algorithm in the following document that i add below

mahalanobis_distance icon mahalanobis_distance

The Mahalanobis distance is a measure of the distance between a point P and a distribution D, introduced by P. C. Mahalanobis in 1936.[1] It is a multi-dimensional generalization of the idea of measuring how many standard deviations away P is from the mean of D. This distance is zero if P is at the mean of D, and grows as P moves away from the mean along each principal component axis. If each of these axes is re-scaled to have unit variance, then the Mahalanobis distance corresponds to standard Euclidean distance in the transformed space. The Mahalanobis distance is thus unitless and scale-invariant, and takes into account the correlations of the data set. i added some more explanation about code right in document file.

min_shift_clustring icon min_shift_clustring

min_shift algorithm is a clustering method .i tried to take closer look on this algorithm

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