Kmeans Lite
2016-08-23
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This is a k means based algorithm. KMEANS K-means clustering.% IDX = KMEANS(X, K) partitions the points in the N-by-P data matrix% X into K clusters. This partition minimizes the sum, over all% clusters, of the within-cluster sums of point-to-cluster-centroid% distances. Rows of X correspond to points, columns correspond to% variables. KMEANS returns an N-by-1 vector IDX containing the% cluster indices of each point. By default, KMEANS uses squared% Euclidean distances.%% [IDX, C] = KMEANS(X, K) returns the K cluster centroid locations in% the K-by-P matrix C.%% [IDX, C, SUMD] = KMEANS(X, K) returns the within-cluster sums of% point-to-centroid distances in the 1-by-K vector sumD.%% [IDX, C, SUMD, D] = KMEANS(X, K) returns distances from each point% to every centroid in the N-by-K matrix D.%% [ ... ] = KMEANS(..., 'PARAM1',val1, 'PARAM2',val2, ...) allows you to% specify optional parameter name/value pairs to control the i
matlab
KMeans
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