Application to familiarize with Linear discriminant analysis and K-means. LDA reduces dimensionality of the data, but unlike PCA it tries to seperate data of different classes rather than just keep the dimensions which keep the most variance. The K-means algorithm is a basic clustering algorithm to cluster a set of data to K clusters, using the euclidean distance between the classes' discriminant (Class mean)
Mobad225/LDA-and-K-means
Application to familiarize with Linear discriminant analysis and K-means. LDA reduces dimensionality of the data, but unlike PCA it tries to seperate data of different classes rather than just keep the dimensions which keep the most variance. The K-means algorithm is a basic clustering algorithm to cluster a set of data to K clusters, using the euclidean distance between the classes' discriminant (Class mean)
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