FindClusters[{e_1, e_2, ...}] partitions the e i into clusters of similar elements. FindClusters[{e_1 -> v_1, e_2 -> v_2, ...}] returns the v_i corresponding to the e i in ...

FindClusters and the distance and dissimilarity functions have been added to the built-in Mathematica kernel. SupDistance is replaced by ChessboardDistance. ...

Cluster analysis is an unsupervised learning technique used for classification of data. Data elements are partitioned into groups called clusters that represent proximate ...

Mathematica includes state-of-the-art algorithms for sequence alignment and comparison, capable of handling strings and lists containing very large numbers of elements.

ColorQuantize[image, n] gives an approximation to image that uses only n distinct colors.

FindCurvePath[{{x_1, y_1}, {x_2, y_2}, ...}] gives an ordering of the {x_i, y_i} that corresponds to one or more smooth curves.

NearestFunction[data] represents a function whose values give the elements closest to an element that is supplied.

Version 6.0 added a collection of carefully optimized functions to Mathematica's powerful arsenal of numerical handling capabilities.

Mathematica represents Boolean expressions in symbolic form, so they can not only be evaluated, but also be symbolically manipulated and transformed. Incorporating ...

MeanShift[list, d] replaces each element in list by the mean of the values of all elements that differ by less than d.