Optimal Classification/clustering

Many people consider clustering the most important unsupervised learning problem. Clustering deals with finding structure in a collection of unlabeled data.

Clustering has many practical applications.

Many clustering algorithms ( k-clustering algorithms ) require a human to specify ahead of time how many buckets (categories, labels, classes, etc.) to divide up the input into.


 * Median cut clustering -- perhaps the fastest clustering algorithm
 * K-means clustering -- a very fast clustering algorithm, with the disadvantage that it does not provide the same result with each run.
 * QT clustering algorithm
 * expectation-maximization algorithm
 * canopy clustering algorithm
 * constrained clustering
 * Fuzzy clustering by Local Approximation of MEmberships (FLAME clustering)
 * self-organizing map
 * vector quantization
 * Learning Vector Quantization