ks for any type of data. We needed a program that would: 1) Fit a mixture of Gaussians with unconstrained covariance matrices 2) Automatically choose the number of mixture components 3) Be robust against noise 4) Reduce the problem of local minima 5) Run fast on large data sets (up to 100000 points, 48 dimensions) Speed in particular was essential. KlustaKwik is based on the CEM algorithm of Celeux and Govaert (which is faster than the standard EM algorithm

KlustaKwik-R1-7
...............\KlustaKwik
...............\..........\Array.h
...............\..........\Debug
...............\..........\.....\param.obj
...............\..........\.....\param.pch
...............\..........\.....\param.pdb
...............\..........\.....\vc60.idb
...............\..........\.....\vc60.pdb
...............\..........\KK.cpp
...............\..........\KK.h
...............\..........\KlustaKwik.cpp
...............\..........\KlustaKwik.h
...............\..........\KlustaKwik.mcp
...............\..........\KlustaSave.h
...............\..........\linux-pentium
...............\..........\.............\KlustaKwik
...............\..........\macosx
...............\..........\......\KlustaKwik
...............\..........\makefile
...............\..........\param.c
...............\..........\param.dsp
...............\..........\param.dsw
...............\..........\param.h
...............\..........\param.ncb
...............\..........\param.opt
...............\..........\param.plg
...............\..........\README.txt
...............\..........\ReleaseNotes.txt
...............\..........\test
...............\..........\....\test.fet.1
...............\..........\....\test.model.1
...............\..........\....\test_res.clu.1
...............\..........\....\test_res.model.1
...............\..........\Windows
...............\..........\.......\KlustaKwik.exe

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