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.

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 efficiently implements state-of-the-art data classification algorithms, allowing you to visualize distributions, search for nearest neighbors, and do cluster ...

Different measures of distance or similarity are convenient for different types of analysis. Mathematica provides built-in functions for many standard distance measures, as ...

Mathematica has uniquely flexible capabilities for processing large volumes of textual data. Most often data represented as a string is converted to lists or other constructs ...