-Means: A Fast Fuzzy Clustering

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.

Dataset	Performance parameters	Clusters	-Means			Fuzzy -Means			-Means
			Euc	Man	Min	Euc	Man	Min	Euc	Man	Min
Iris	Precision	1	0.898	0.898	0.898	0.858	0.905	0.861	0.627	0.627	1
		2	1	1	0.645	0.655	0.691	1	1	1	0.895
		3	0.644	0.645	1	1	1	0.670	1	1	0.631
		Average	0.847	0.847	0.848	0.837	0.865	0.844	0.876	0.876	0.842
		Accuracy	0.880	0.880	0.880	0.880	0.899	0.796	0.796	0.880	0.874
Lens	Precision	1	0.167	0.167	0.25	0.286	0.267	0.278	0.200	0.238	0.286
		2	0.273	0.200	0.238	0.200	0.250	0.25	0.167	0.286	0
		3	0.250	0.286	0.278	0.167	0.200	0.238	0.295	0	0.238
		Average	0.230	0.217	0.255	0.217	0.239	0.255	0.221	0.175	0.175
		Accuracy	0.522	0.507	0.547	0.547	0.533	0.547	0.504	0.504	0.504
Wine	Precision	1	0.356	0.517	0.957	0.345	0.858	0.957	0.360	0.504	1
		2	0.595	0.430	0.356	0.577	0.605	0.577	0.605	1	0.595
		3	0.957	1	0.595	0.956	0.386	0.345	0.957	0.556	0.338
		Average	0.636	0.649	0.636	0.626	0.616	0.626	0.641	0.687	0.644
		Accuracy	0.719	0.685	0.719	0.719	0.735	0.711	0.720	0.686	0.695