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 further reading cluster analysis Artificial Neural Networks/Clustering Proteomics/Protein Chips/Algorithms