1.冰山立方体知识：
waiting...
2.代码实现：
实现思路：全程模拟，写的时候理解还不够透彻，第一轮算出频率大于min_sup的集合，然后根据此集合开始不断向下扩展，筛选所有大于min_sup的项直至达到最大维数。实现过程遵循的原则：所有出现在数据中但未出现在满足min_sup的集合中的项一律不予计算未实现：理想地，应当首先处理最有区分能力的维。维应当以基数递减数处理。基数越高，分区越小，因而分区越多，从页为BUC剪枝提供了更大的机会。代码：
test.csv:
a1,b1,c1,d1a1,b2,c2,d2a1,b3,c2,d1a1,b4,c1,d2a2,b1,c1,d1a2,b2,c2,d2a2,b3,c2,d1a2,b4,c1,d2a3,b1,c1,d1a3,b2,c2,d2a3,b3,c2,d1a3,b4,c1,d2a4,b1,c1,d1a4,b2,c2,d2a4,b3,c1,d2a4,b4,c2,d1
BUC:
with open('test.csv','r') as fr:data_count = dict()data = fr.read().splitlines()for i in range(len(data)):data[i] = str(data[i]).split(",")dims = [x[0] for x in data[0]] # record representation of each dimensionmin_sup = 3 # minimum support valuedef BUC(sub, d):# reach the max dimensionif d > len(dims):returnsub_list = list(sub) # list store like ['a1 b1','a2','b2 c1']sub_list = [x.split(" ") for x in sub_list]for x in sub_list:if len(x) == d-1: # expected to expandfor y in sub_list:if len(y) == 1 and y[0] not in x: # single item and not repeatitem_cnt = 0for line in data:minn = 0x3ffffor xx in x:minn = min(minn, line.count(xx)) # count the longer frequencyminn = min(minn, line.count(y[0])) # count the single frequencyitem_cnt += minn # total frequency of new itemitem = " ".join(x + y) # reform item# filter repeatfor key in sub:# filter item like "a1 c1" and "c1 a1"flag = (sorted(item.split(' ')) == sorted(key.split(' ')))item_cnt = -1 if flag is True else item_cnt# add to total sub dictif item_cnt >= min_sup:sub[item] = item_cntprint(d, ":", sub)BUC(sub, d+1)# calculate the single item frequencyfor line in data:for char in line:if char not in data_count:data_count[char] = 1else:data_count[char] += 1sub = {a: data_count[a] for a in data_count if data_count[a] >= min_sup}print(1, ":", sub)# start BUCBUC(sub, 2)
感觉自己好久没写代码了...实在是太菜了
本来想直接借鉴@yanjiaxin1996，太长了没看下去2333
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