The Design of Academic Programs Using Rough Set Association Rule Mining

Table 2

Support and confidence measures using Apriori and rough Apriori.

ID	PEO	a	b	c	d	e	f	G	H	i	J	k
1	C_D	1	1	1	1	1	1	1	1	1	1	1
2	G_S	1	1	1	0	1	1	0	0	1	0	0
3	P	0	0	1	0	1	0	0	0	1	1	1
4	E	0	0	1	0	0	1	0	0	0	0	0
5	T_C	0	0	0	0	0	0	0	1	1	1	1
6	L	0	0	0	1	0	1	1	0	0	1	0
7	K_C	1	1	1	1	0	0	0	1	0	1	0
8	L_L_L	0	0	0	0	0	1	1	0	1	1	0
9	S	0	0	0	0	1	1	1	1	1	1	0
10	L	0	0	1	1	0	0	1	0	0	0	0
11	C_D	1	0	1	1	0	1	0	0	1	1	1
12	T	0	1	0	1	0	0	1	0	0	0	0
13	P	0	1	1	0	1	0	1	1	1	1	1
14	E	0	0	1	0	0	1	0	0	1	0	0
15	C	0	0	1	1	0	0	1	0	0	0	0

Apriori association rule algorithms s% = S = occurrence/total trans, total number of trans = 15, s ({ab}) = 3/15 = 60%, s ({b c}) = 4/15 = 40%, and conf (a ⇒ b) = 3/4 = 75%, conf (b ⇒ c) = 4/5 = 80%.
Rough set Apriori association rule algorithm RSAR, s ({c e}) = 2, {decSet} = {[ɛ]d} = [{P}] = {3,13} and s {[ɛ]d} = 2, {condSet} = {[ɛ]c} = [{c, e}] = {3,13} and s ([ɛ]c) = 2, and s of RSAR = s {condSet ∪ decSet} = s {[ɛ]c, [ɛ]d} = s {3,13} = 2, |Ω| = 15. Then, s of RSAR is computed as ([ɛ]d)/|Ω| = 2/15 = 13%, and the confidence of RSAR is S (RSAR)/s ([ɛ]c) = 2/2 = 100%.