% This Fundamental Clustering Problems Suite (FCPS) may serve as a minimal test for new invented cluster algorithms. % % Every new invented algorithm should at least cluster the FCPS correctly. % % The data sets may be used as long as the following citation is referenced in any publication % concering any of this data: % % Alfred Ultsch, Clustering with SOM: U*C, in Proc. Workshop on Self Organizing Feature Maps ,pp 31-37 Paris 2005. % % main problem: almost touching clusters % @relation tetra @ATTRIBUTE x REAL @ATTRIBUTE y REAL @ATTRIBUTE z REAL @ATTRIBUTE class {1,2,3,4} @data 1.295428,0.050829,-0.385217,1 1.409178,-0.035191,-0.25198,1 1.096803,0.246365,-0.415011,1 1.463328,0.265354,-0.513488,1 1.603284,0.080577,-0.470257,1 1.001833,-0.083266,-0.681399,1 1.517445,0.286702,-0.547891,1 1.617901,-0.158504,-0.301126,1 1.083162,-0.371103,-0.358668,1 1.669991,0.0866,-0.280595,1 1.457644,-0.401268,-0.407497,1 0.914329,0.242193,-0.310915,1 1.643737,0.153696,-0.241876,1 1.595419,-0.310447,-0.368662,1 1.153698,-0.25427,-0.87687,1 1.524989,0.162923,-0.877225,1 1.247658,-0.333062,-0.037487,1 0.826074,-0.31543,-0.389405,1 0.877268,0.354988,-0.618269,1 0.809343,0.313444,-0.296642,1 1.546564,0.055433,0.057049,1 1.555207,0.48368,-0.659155,1 1.570894,-0.469775,-0.218926,1 0.9618,0.345034,-0.838026,1 0.948567,-0.327407,-0.020961,1 1.527893,-0.082015,-1.018224,1 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