% 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: different variances and inter cluster distances % @relation lsun @ATTRIBUTE x REAL @ATTRIBUTE y REAL @ATTRIBUTE class {1,2,3} @data 3.277701,0.814082,1 0.387577,0.17678,1 0.268546,0.582963,1 2.031145,0.244597,1 0.188677,0.46128,1 3.525472,0.265579,1 2.229583,0.682189,1 3.392074,0.420976,1 3.589815,0.832527,1 1.227873,0.810909,1 1.300699,0.146538,1 3.596968,0.421791,1 3.106757,0.779664,1 0.221139,0.612603,1 1.496841,0.495985,1 2.860498,0.535719,1 1.45071,0.142352,1 1.745799,0.066373,1 1.4048,0.32968,1 1.7696,0.777809,1 0.464511,0.672442,1 2.750803,0.16552,1 1.571006,0.091428,1 1.18166,0.857799,1 0.661957,0.612812,1 0.828567,0.798048,1 3.313914,0.216602,1 3.625645,0.538592,1 3.611238,0.584716,1 2.904519,0.465036,1 3.080431,0.24549,1 3.851167,0.574214,1 0.944956,0.077006,1 3.834215,0.224878,1 2.179064,0.784896,1 2.716449,0.944268,1 0.02978,0.931252,1 0.095525,0.349743,1 3.835506,0.889721,1 1.142732,0.811691,1 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