Apriori算法介绍（Python实现）

Args:

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

k: Maximum number of items for all frequent itemsets.

min_support: The minimum support.

Returns:

L: The list of Lk.

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

"""

support_data = {}

C1 = create_C1(data_set)

L1 = generate_Lk_by_Ck(data_set, C1, min_support, support_data)

Lksub1 = L1.copy()

L = []

L.append(Lksub1)

for i in range(2, k+1):

Ci = create_Ck(Lksub1, i)

Li = generate_Lk_by_Ck(data_set, Ci, min_support, support_data)

Lksub1 = Li.copy()

L.append(Lksub1)

return L, support_data

def generate_big_rules(L, support_data, min_conf):

"""

Generate big rules from frequent itemsets.

Args:

L: The list of Lk.

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

min_conf: Minimal confidence.

Returns:

big_rule_list: A list which contains all big rules. Each big rule is represented

as a 3-tuple.

"""

big_rule_list = []

sub_set_list = []

for i in range(0, len(L)):

for freq_set in L[i]:

for sub_set in sub_set_list:

if sub_set.issubset(freq_set):

conf = support_data[freq_set] / support_data[freq_set - sub_set]

big_rule = (freq_set - sub_set, sub_set, conf)

　　推荐阅读

　　家用NAS有什么用?充分挖掘你的NAS功能

家用NAS有什么竽暌姑?具体整顿如下：1. 存储所有照片并分类整顿。2. 建立本身的视频办事器，出差在外可以播放家里的视频、音频，看照片。3. 存储大年夜量音乐，经由过程光纤声卡直接连到音>>>详细阅读

地址：http://www.17bianji.com/lsqh/34841.html