Table 3. Clustering accuracy and accuracy based performance improvement for ten clustering algorithms.

Algorithm	Method	Accuracy (%)
		D1, PCA	D1, WAV	D2, PCA	D2, WAV	D3, PCA	D3, WAV
Meanshift	Proposed	89.89	97.81	83.76	94.03	72.18	81.82
	Conventional	89.18	97.59	62.36	94	52.72	75.05
	Improved Acc. (%)	0.71	0.23	21.4	0.03	19.46	6.76
DBSCAN	Proposed	87.85	92.16	34.72	84.02	72.18	72.19
	Conventional	61.07	35.29	33.79	62.3	51.55	51.88
	Improved Acc. (%)	26.77	56.87	0.93	21.72	20.63	20.32
Kmeans	Proposed	66.35	99.38	95.21	92.78	71.63	78.87
	Conventional	44.69	63.86	65.46	81.58	36.56	62.21
	Improved Acc. (%)	21.66	35.52	29.76	11.19	35.06	16.66
Kmedoids	Proposed	98.07	99.38	77.52	93.1	61.31	83.92
	Conventional	96.79	99.38	48.46	92.78	43.48	69.14
	Improved Acc. (%)	1.28	0	29.06	0.32	17.82	14.78
VBGMM	Proposed	88.25	77.31	62.3	84.34	70.08	63.51
	Conventional	85.26	66.47	54.52	68.85	50.66	31.93
	Improved Acc. (%)	2.98	10.85	7.77	15.49	19.42	31.58
EMGMM	Proposed	91.31	90.37	72.97	84.66	74.15	57.21
	Conventional	89.15	80.41	56.61	65.84	61.95	38.15
	Improved Acc. (%)	2.16	9.97	16.36	18.82	12.2	19.05
Agglomerative	Proposed	94.55	99.26	88.46	96	56.15	80.88
	Conventional	93.7	99.21	84.86	87.91	43.75	51.4
	Improved Acc. (%)	0.85	0.06	3.6	8.09	12.4	29.48
OPTICS	Proposed	76.58	29.42	31	26.04	62.33	20.58
	Conventional	42.67	17.18	22.71	12.38	12.34	4.28
	Improved Acc. (%)	33.9	12.24	8.29	13.66	49.99	16.29
BIRCH	Proposed	93.19	99.26	86.83	93.33	54.73	80.88
	Conventional	72.43	99.18	84.66	92.89	44.76	55.53
	Improved Acc. (%)	20.76	0.09	2.18	0.44	9.96	25.35
FCM	Proposed	71.78	99.38	84.98	92.63	60.99	77.15
	Conventional	71.72	99.38	49.42	92.6	39.77	63.97
	Improved Acc. (%)	0.06	0	35.56	0.03	21.22	13.18