Table 2: Performance analysis of KM, CM, and KCM clustering techniques on data and benchmarks Iris, Lens, and Wine datasets. Note that each data has 3 clusters; the best precision and accuracy for each data set is in bold font.

DatasetPerformance parametersClusters-MeansFuzzy -Means-Means
EucManMinEucManMinEucManMin

IrisPrecision10.8980.8980.8980.8580.9050.8610.6270.6271
2110.6450.6550.6911110.895
30.6440.6451110.670110.631
Average0.8470.8470.8480.8370.8650.8440.8760.8760.842
Accuracy0.8800.8800.8800.8800.8990.7960.7960.8800.874

LensPrecision10.1670.1670.250.2860.2670.2780.2000.2380.286
20.2730.2000.2380.2000.2500.250.1670.2860
30.2500.2860.2780.1670.2000.2380.29500.238
Average0.2300.2170.2550.2170.2390.2550.2210.1750.175
Accuracy0.5220.5070.5470.5470.5330.5470.5040.5040.504

WinePrecision10.3560.5170.9570.3450.8580.9570.3600.5041
20.5950.4300.3560.5770.6050.5770.60510.595
30.95710.5950.9560.3860.3450.9570.5560.338
Average0.6360.6490.6360.6260.6160.6260.6410.6870.644
Accuracy0.7190.6850.7190.7190.7350.7110.7200.6860.695