K-means clustering

Classification k means in matlab

The following Matlab project contains the source code and Matlab examples used for classification k means. we built the matrix that let us watch the variations of distances of one class to another class, allowing search for a uniform distribution of distances for make a correct classification of unknown data system

Performs kmedioids clustering, requires only a nxn distance matrix d and number of clusters, k. in matlab

The following Matlab project contains the source code and Matlab examples used for performs kmedioids clustering, requires only a nxn distance matrix d and number of clusters, k. . Performs k-mediods clustering; only requires a distance matrix D and number of clusters k.

K Means Clustering Matlab Code

k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. 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. This results in a partitioning of the data space into Voronoi cells.

Fuzzy k means in matlab

The following Matlab project contains the source code and Matlab examples used for fuzzy k means. Codes for fuzzy k means clustering, including k means with extragrades, Gustafson Kessel algorithm, fuzzy linear discriminant analysis. Performance measure is also calculated.

Fast k means in matlab

The following Matlab project contains the source code and Matlab examples used for fast k means. [L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix X into k (or, rarely, fewer) clusters by applying the well known batch K-means algorithm.

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