mlpack
1.0.6

Introduction
The Euclidean Minimum Spanning Tree problem is widely used in machine learning and data mining applications. Given a set of points in , our task is to compute lowest weight spanning tree in the complete graph on with edge weights given by the Euclidean distance between points.
Among other applications, the EMST can be used to compute hierarchical clusterings of data. A singlelinkage clustering can be obtained from the EMST by deleting all edges longer than a given cluster length. This technique is also referred to as a FriendsofFriends clustering in the astronomy literature.
mlpack includes an implementation of DualTree Boruvka on trees, the empirically and theoretically fastest EMST algorithm. For more details, see March, et al., Euclidean Minimum Spanning Tree: Algorithm, Analysis, and Applications, in KDD, 2010. An implementation using cover trees is forthcoming.
mlpack provides:
 a simple commandline executable to compute the EMST of a given data set
 a simple C++ interface to compute the EMST
Table of Contents
A list of all the sections this tutorial contains.
 Introduction
 Table of Contents
 CommandLine 'EMST'
 The 'DualTreeBoruvka' class
 Further documentation
CommandLine 'EMST'
The emst executable in mlpack will compute the EMST of a given set of points and store the resulting edge list to a file.
The output file contains an edge list representation of the MST in an matrix, where the first and second columns are labels of points and the third column is the edge weight. The edges are sorted in order of increasing weight.
Below are several examples of simple usage (and the resultant output). The 'v' option is used so that verbose output is given. Further documentation on each individual option can be found by typing
The code performs at most iterations for data points. It will print an update on the number of MST edges found after each iteration. Convenient program timers are given for different parts of the calculation at the bottom of the output, as well as the parameters the simulation was run with.
The input points are labeled 05. The output tells us that the MST connects point 0 to point 3, point 4 to point 5, point 1 to point 3, point 1 to point 2, and point 2 to point 4, with the corresponding edge weights given in the third column. The total squared length of the MST is also given in the verbose output.
Note that it is also possible to compute the EMST using a naive ( ) algorithm for timing and comparison purposes.
The 'DualTreeBoruvka' class
The 'DualTreeBoruvka' class contains our implementation of the DualTree Boruvka algorithm.
The class has two constructors: the first takes the data set, constructs the tree, and computes the MST. The second takes data set and an already constructed tree.
The class provides one method that performs the MST computation:
This method stores the computed MST in the matrix results in the format given above.
Further documentation
For further documentation on the DualTreeBoruvka class, consult the complete API documentation.
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