A whale optimization algorithm (WOA) approach for clustering

Figures & data

Figure 1. The ART datset and clustering of ART data set.

Table 1. Comparison of the performance of WOA clustering algorithm with other well-known clustering algorithms

Data set	Criteria	k-Means	DE	GA	ABC	PSO	WOA
	Mean	374.87	271.4227	271.2278	271.3474	271.2276	268.8007
ART	SD	123.04	0.25	0.00017	0.16	0	0.74
	Rank	6	5	3	4	2	1
	Mean	103.58	125.3395	120.0766	97.0986	97.4058	96.7993
Iris	SD	12.48	1.13	0.36	0.43	0.26	0.10
	Rank	3	5	4	2	3	1
	Mean	18,560.00	16,530.11	16,493.9314	16,394.8211	16,292.96	16,295.00
Wine	SD	2869	1.32	0.75	1.29	8.05	0.72
	Rank	6	5	4	3	1	2
	Mean	5853.57	5794.273	5732.8690	5643.8491	5532.096	5539.72
CMC	SD	1.63	30.90	0.64	10.00	14.78	0.79
	Rank	6	5	4	3	1	2
	Mean	1427.52	1427.869	1425.489	1429.9450	1424.754	1423.8
Balance	SD	3.49	1.05	0.94	1.32	0.35	0.98
	Rank	4	5	3	6	2	1
	Mean	3262.35	3237.142	3237.9260	3036.9551	3037.963	3036.12
Cancer	SD	0.16	0.19	0.25	0.048	6.44	0.20
	Rank	6	4	5	2	3	1
	Mean	255.073	261.0285	252.2135	256.0595	240.8885	231.2912
Glass	SD	4.72	7.66	1.56	2.94	12.06	4.51
	Rank	5	6	3	4	2	1
	Mean	1995.189	1877.237	1888.209	1897.4211	1890.2071	1870.93
Thyroid	SD	10.78	6.05	6.90	6.62	0	1.3
	Rank	6	2	3	5	4	1

Figure 2. Convergence of curves of the WOA and PSO for some data sets.