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资源说明：This program contains two different methods to get strong approximate solutions to the NP hard clustering problem in n^2 time
GA & K-means clustering test Author: Spencer Dawson (except for MersenneTwister.h which is written by Richard J. Wagner) Purpose: This program contains two different methods to get strong approximate solutions to the NP hard clustering problem in n^2 time GA: Genetic Algorithms This solution uses a simple GA with adjustable parameters (in ga_k-means_clustering.h) to find a solution through directed search K-means clustering: This solution uses a slightly modified version of the standard k-means clustering algorithm to adjust randomly initialized centers by iteratively assigning each point in the data set to the nearest center(generated cluster) then adding the point closest to each center to it's data set. Finally each center is moved toward the center of the points in it's data set. Notes on compiling: This was written as a Qt Creator project. And originally compiled using gcc through minigw. Files: ga_k-means_clustering.pro The QT project file main.cpp the source file for the executable. Starts the GUI and does a test of the random number generator (MersenneTwister.h) mainwindow.ui/mainwindow.h/mainwindow.cpp The GUI ga_k-means_clustering.h/ga_k-means_clustering.cpp This control structure simplifies/combines the GA and K-means for the gui cluster.h/cluster.cpp provides common code between the different cluster types gacluster.h/gacluster.cpp inherites from cluster.h provides extra code to interface with GA.h kmeans.h/kmeans.cpp provides extra code required for the k-means algorithm ga.h a GA template adapted (slightly) for this implementation mypoint.h/mypoint.cpp used by the gui to help with plotting
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