利用python实现《数据挖掘——概念与技术》一书中描述的Apriori算法

 from itertools import combinations

 data = [['I1', 'I2', 'I5'], ['I2', 'I4'], ['I2', 'I3'], ['I1', 'I2', 'I4'], ['I1', 'I3'],
         ['I2', 'I3'], ['I1', 'I3'], ['I1', 'I2', 'I3', 'I5'], ['I1', 'I2', 'I3']]

 # 候选集生成
 # 输入：
 # f_set: k-1项集, k:项集个数
 # 输出：
 # k_cand：k项候选集
 def apriori_gen(f_set, k):
     k_cand = []
     temp = [frozenset(l) for l in combinations(f_set, k)]
     for t in temp:
         if has_infrequent_subset(t, f_set):
             del t
         else:
             k_cand.append(t)
     return k_cand

 # 非频繁项集的超集也是非频繁的
 def has_infrequent_subset(c_set, f_set):
     for subset in c_set:
         if not frozenset([subset]).issubset(f_set):
             return True
     return False

 # 输入（绝对）最小支持度, min_sup
 # 输出：全部频繁项集（不包括一项集）, all_f_set
 def get_f_set(min_sup=2):
     all_f_set = []
     L1 = frozenset([d for ds in data for d in ds])
     k = 2
     size = len(L1)
     while k <= size:
         c_k = frozenset(apriori_gen(L1, k))
         for c in c_k:
             count = 0
             for d in data:
                 if c.issubset(frozenset(d)):
                     count += 1
             if count >= min_sup:
                 all_f_set.append((c, count))
         k += 1
     return all_f_set

 if __name__ == '__main__':
     all_frequent_set = get_f_set()
     for i in all_frequent_set:
         print(i)