K-Means (K-means clustering). The data given from input file is clustered by the k-means method, which aims to partition the points into k groups such that the sum of squares from points to the assigned cluster centres is minimized. At the minimum, all cluster centres are at the mean of their Voronoi sets (the set of data points which are nearest to the cluster centre).

This program use statistical functions from "R" free software environment for statistical computing and graphics (http://www.r-project.org).

This program requires the R-package to be installed on your computer.

Input file should contain table of numerical data: lines for observations (cases) columns should be separated by tabulation or user-defines symbol (; , etc); for example, if comma (,) separator is used, the file format is the same as the CSV (comma separated values) format. No missing data allowed.

Example of input data file format:

ItemName   Feat1	Feat2		Feat3		Feat4		Feat5	   ClassVar
Item1	-11.761101	-5.295846	-2.491684	4.151158	9.777093	1
Item2	-11.425886	-6.753716	0.136692	5.161748	13.618702	1
Item3	-7.069796	0.545457	0.097140	0.678579	10.302988	1
Item4	-13.480880	-3.867702	0.119297	2.333842	10.992096	1
Item5	-9.707938	-2.597949	-2.329997	2.928526	8.441053	1
Item6	-10.013794	-2.165258	-3.169195	2.625904	10.611103	1
Item7	-9.057161	-4.766594	1.691733	1.655782	7.046236	1
Item8	-8.562761	-1.272652	-3.990204	2.286294	12.768212	1
Item9	-12.724631	-4.710623	-2.114719	2.812189	6.434645	1
Item10	-9.593738	-5.478652	-1.799524	4.306497	9.514756	1
Item11	-7.699759	-1.546648	-0.423322	4.889767	9.228675	1
Item12	-13.158116	-2.891354	0.595935	2.264199	12.004761	1
Item13	-10.509598	-3.414075	-1.962310	1.263863	10.199896	1
Item14	-6.547624	-3.594928	-2.117222	5.168950	10.838221	1
Item15	-12.375988	-3.130436	-2.169164	1.537614	11.112888	1
Item16	-12.953032	-2.805048	0.085116	3.303354	7.405194	1
Item17	-11.370708	-2.848384	-0.848201	3.885525	10.569231	1
Item18	-13.117222	-7.025575	1.406507	7.069338	12.230415	1
Item19	-11.573168	0.288003	-2.826167	4.397137	10.851711	1
Item20	-7.993835	-1.204352	-1.924345	0.829829	10.314768	1
Item21	-9.225135	-2.512925	-1.608051	1.420301	9.766411	1
Item22	-8.402783	-0.890500	3.189703	3.754479	7.481063	1
Item23	-9.888180	-3.345775	1.965667	2.906369	11.488815	1
Item24	-11.686270	-5.389477	2.556932	1.661153	9.717826	1
Item25	-12.599567	-0.266091	-3.936308	0.751762	10.405225	1
Item26	-11.365093	-1.919706	-0.458052	1.861843	9.521104	1
Item27	-11.027619	-2.944884	-2.792962	4.144322	7.958556	1
Item28	-11.795160	-6.769646	0.908383	1.005066	11.240333	1
Item29	-13.629933	0.674184	-3.386853	-0.095859	10.490432	1
Item30	-7.823298	-5.452589	-2.336894	1.919889	9.421125	1
Item31	6.360118	6.794549	4.168188	-4.492538	-12.297555	0
Item32	8.774682	0.492721	1.587909	-5.486587	-12.361278	0
Item33	7.768181	3.989776	3.289377	-0.895444	-13.067171	0
Item34	8.581133	2.922361	3.952544	-4.450362	-6.787133	0
Item35	6.176519	4.526292	-2.771599	-3.477187	-7.316202	0
Item36	11.539781	-0.892880	2.868221	-1.456557	-11.008881	0
Item37	11.743034	3.527726	-0.635792	-2.067965	-7.151524	0
Item38	10.527299	1.460768	0.862300	-1.967742	-8.819727	0
Item39	8.148808	5.157964	-0.916135	-1.551958	-9.467513	0
Item40	9.241432	1.483108	-0.981933	1.046571	-8.504166	0
Item41	9.444197	4.963927	1.127201	-0.523484	-9.102817	0
Item42	11.545396	4.604968	4.818171	-5.046815	-13.494675	0
Item43	11.890988	1.220710	-2.069796	-2.942747	-8.996673	0
Item44	11.810480	2.031465	2.987976	-5.699606	-10.026246	0
Item45	10.806543	5.275155	4.969420	-2.792596	-11.345561	0
Item46	10.261177	3.586077	3.340220	-3.339244	-7.795038	0
Item47	8.407544	2.887997	3.104312	-3.734519	-9.758477	0
Item48	7.317484	5.553850	1.618000	-2.525315	-13.613147	0
Item49	10.654500	2.579577	1.922452	-3.765160	-10.414136	0
Item50	6.940641	3.525834	-0.660756	-4.105869	-10.064455	0