Associative rule mining using FP-growth algorithm

FP growth algorithm is an improved version of the apriori algorithm. FP growth algorithm is used for finding frequent itemset in a transaction database. Unlike Apriori algorithm, we don’t generate candidate subsets in FP-growth algorithm.

FP growth represents frequent items in frequent pattern trees or FP-tree.

Id, Items
1, f, a, c, d, g, m, p
2, a, b, c, f, l, m, o
3, b, f, h, o
4, b, k, c, p
5, a, f, c, l, p, m, n

items, support
a, 3
b, 3
c, 4
d, 1
f, 4
g, 1
k, 1
l, 2
m, 3
n, 1
o, 2
p, 3

min. support = 3

items, support
a, 3
b, 3
c, 4
f, 4
m, 3
p, 3

order the items

items, support
f,4
c, 4
a, 3
b, 3
m, 3
p, 3

{f, c, a, b, m, p}

{Id}, {Items}, {Ordered Items}
{1}, {f, a, c, d, g, m, p}, {f, c, a, m, p}
{2}, {a, b, c, f, l, m, o}, {f, c, a, b, m}
{3}, {b, f, h, o}, {f, b}
{4}, {b, k, c, p}, {c, b, p}
{5}, {a, f, c, l, p, m, n}, {f, c, a, m, p}

f: 4
c: 4
a: 3
b: 3
m: 3
p: 3