Apriori算法介绍（Python实现）

in range(1, len_Lksub1):

l1 = list(list_Lksub1[i])

l2 = list(list_Lksub1[j])

l1.sort()

l2.sort()

if l1[0:k-2] == l2[0:k-2]:

Ck_item = list_Lksub1[i] | list_Lksub1[j]

# pruning

if is_apriori(Ck_item, Lksub1):

Ck.add(Ck_item)

return Ck

def generate_Lk_by_Ck(data_set, Ck, min_support, support_data):

"""

Generate Lk by executing a delete policy from Ck.

Args:

data_set: A list of transactions. Each transaction contains several items.

Ck: A set which contains all all frequent candidate k-itemsets.

min_support: The minimum support.

support_data: A dictionary. The key is frequent itemset and the value is support.

Returns:

Lk: A set which contains all all frequent k-itemsets.

"""

Lk = set()

item_count = {}

for t in data_set:

for item in Ck:

if item.issubset(t):

if item not in item_count:

item_count[item] = 1

else:

item_count[item] += 1

t_num = float(len(data_set))

for item in item_count:

if (item_count[item] / t_num) >= min_support:

Lk.add(item)

support_data[item] = item_count[item] / t_num

return Lk

def generate_L(data_set, k, min_support):

"""

Generate all frequent itemsets.

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