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This is an implementation of the paper
k-means++: the advantages of careful seeding.
It converges very quickly.
Efficient implementation of K-medoids clustering methods. This method is similar to K-means but more robust.
For more detail, please see
Input data are assumed column vectors.
This function performs kernel version of kmeans algorithm. When the linear kernel (i.e., inner product) is used, the algorithm is equivalent to standard kmeans algorithm.
K: n x n a semi-definite matrix computed by a kernel...
This is a function performs maximum likelihood estimation of Gaussian mixture model by using expectation maximization algorithm.
It can work on data of arbitrary dimensions. Several techniques are applied in order to avoid the float...
Transform a inner product matrix or kernel matrix to a square of distance matrix
This is a very fast implementation of the original kmeans clustering algorithm without any fancy acceleration technique, such as kd-tree indexing and triangular inequation. (actually the fastest matlab implementation as far as I can tell.)
This is just a brute force implementation of k nearest neighbor search without using any fancy data structure, such as kd-tree. However it is the fastest knn matlab implementation I can find.
A partial sort mex function is implemented...
Normalized mutual information is often used for evaluating clustering result, information retrieval, feature selection etc. This is a optimized implementation of the function which has no for loops.
high accuracy version of log(sum(exp(x)))
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