Table 2.

c _max⁡ estimated by several methods. n is the number of objects in the dataset, c is the actual number of clusters, c _ER is the number of clusters estimated by the empirical rule, that is, $⌊\sqrt{n}⌋$, c _AP is the number of clusters obtained by AP algorithm, and c _DBA is the number of clusters obtained by density-based algorithm.

					c DBA
Dataset	n	c	c ER	c AP	p = 1	p = 2	p = 3	p = 5
Iris	150	3	12	9	20	14	9	6
Wine	178	3	13	15	14	7	6	3
Seeds	210	3	14	13	18	12	5	2
SubKDD	1050	6	32	24	21	17	10	7
SD1	200	20	14	19	38	22	20	—
SD2	2000	4	44	25	16	3	4	2
SD3	885	3	29	27	24	19	5	3
SD4	947	3	30	31	23	13	8	4