最近在學資料探勘，學完Apriori演算法之後就一直想用程式碼實現出來，於是學了幾天python之後，進行了實現...

直接貼程式碼，程式碼中有自己的解釋。

'''
# Filename: MyApriori.py
# author: 薄層
# Date: 2018/5/20 0:41
'''

def load_data_set():
	data_set = [['e1', 'e2', 'e5'], 
				['e2', 'e4'],
				['e2', 'e3'],
				['e1', 'e2', 'e4'],
				['e1', 'e3'],
				['e2', 'e3'],
				['e1', 'e3'],
				['e1', 'e2', 'e3', 'e5'],
				['e1', 'e2', 'e3']]
	return data_set

def Create_C1(data_set):
	'''
	引數：資料庫事務集
	'''
	C1 = set()
	for t in data_set:
		for item in t:
			item_set = frozenset([item])
			# 為生成頻繁專案集時掃描資料庫時以提供issubset()功能
			C1.add(item_set)
	return C1

def is_apriori(Ck_item, Lk_sub_1):
	'''
	引數：候選頻繁k項集，頻繁k-1項集
	'''
	for item in Ck_item:
		sub_item = Ck_item - frozenset([item])
		if sub_item not in Lk_sub_1:
			return False
	return True

def Create_Ck(Lk_sub_1, k):
	'''
	# 引數：頻繁k-1項集，當前要生成的候選頻繁幾項集
	'''
	Ck = set()
	len_Lk_sub_1 = len(Lk_sub_1)
	list_Lk_sub_1 = list(Lk_sub_1)
	for i in range(len_Lk_sub_1): #i: [0, len_Lk_sub_1)
		for j in range(i+1, len_Lk_sub_1): #j: [i+1, len_Lk_sub_1)
			l1 = list(list_Lk_sub_1[i])
			l2 = list(list_Lk_sub_1[j])
			l1.sort()
			l2.sort()
			# 判斷l1的前k-1-1個元素與l2的前k-1-1個元素對應位是否全部相同
			# list[s:t]：擷取s到t範圍的元素生成一個新list
			if l1[0:k-2] == l2[0:k-2]:
				Ck_item = list_Lk_sub_1[i] | list_Lk_sub_1[j]
				if is_apriori(Ck_item, Lk_sub_1):
					Ck.add(Ck_item)
	return Ck

def Generate_Lk_By_Ck(data_set, Ck, min_support, support_data):
	'''
	引數：資料庫事務集，候選頻繁k項集，最小支援度，專案集-支援度dic
	'''
	Lk = set()
	# 通過dic記錄候選頻繁k項集的事務支援個數
	item_count = {}
	for t in data_set:
		for Ck_item in Ck:
			if Ck_item.issubset(t):
				if Ck_item not in item_count:
					item_count[Ck_item] = 1
				else:
					item_count[Ck_item] += 1
	data_num = float(len(data_set))
	for item in item_count:
		if(item_count[item] / data_num) >= min_support:
			Lk.add(item)
			support_data[item] = item_count[item] / data_num
	return Lk
	
def Generate_L(data_set, max_k, min_support):
	'''
	引數：資料庫事務集，求的最高專案集為k項，最小支援度
	'''
	# 建立一個頻繁專案集為key，其支援度為value的dic
	support_data = {}
	C1 = Create_C1(data_set)
	L1 = Generate_Lk_By_Ck(data_set, C1, min_support, support_data)
	Lk_sub_1 = L1.copy() # 對L1進行淺copy
	L = []
	L.append(Lk_sub_1) # 末尾新增指定元素
	for k in range(2, max_k+1):
		Ck = Create_Ck(Lk_sub_1, k)
		Lk = Generate_Lk_By_Ck(data_set, Ck, min_support, support_data)
		Lk_sub_1 = Lk.copy()
		L.append(Lk_sub_1)
	return L, support_data

def Generate_Rule(L, support_data, min_confidence):
	'''
	引數：所有的頻繁專案集，專案集-支援度dic，最小置信度
	'''
	rule_list = []
	sub_set_list = []
	for i in range(len(L)):
		for frequent_set in L[i]:
			for sub_set in sub_set_list:
				if sub_set.issubset(frequent_set):
					conf = support_data[frequent_set] / support_data[sub_set]
					# 將rule宣告為tuple
					rule = (sub_set, frequent_set-sub_set, conf)
					if conf >= min_confidence and rule not in rule_list:
						rule_list.append(rule)
			sub_set_list.append(frequent_set)
	return rule_list

if __name__ == "__main__":
	data_set = load_data_set()
	'''
	print("Test")
	# 資料庫事務列印
	for t in data_set:
		print(t)
	'''
	'''
	print("Test")
	# 候選頻繁1項集列印
	C1 = Create_C1(data_set)
	for item in C1:
		print(item)
	'''
	'''
	# 頻繁1項集列印
	print("Test")
	L = Generate_L(data_set, 1, 0.2)
	for item in L:
		print(item)
	'''
	'''
	# 頻繁k項集列印
	print("Test")
	L, support_data = Generate_L(data_set, 2, 0.2)
	for item in L:
		print(item)
	'''
	'''
	# 關聯規則測試
	print("Test")
	L, support_data = Generate_L(data_set, 3, 0.2)
	rule_list = Generate_Rule(L, support_data, 0.7)
	for item in support_data:
		print(item, ": ", support_data[item])
	print("-----------------------")
	for item in rule_list:
		print(item[0], "=>", item[1], "'s conf:", item[2])
	'''
	
	L, support_data = Generate_L(data_set, 3, 0.2)
	rule_list = Generate_Rule(L, support_data, 0.7)
	for Lk in L:
		print("="*55)
		print("frequent " + str(len(list(Lk)[0])) + "-itemsets\t\tsupport")
		print("="*55)
		for frequent_set in Lk:
			print(frequent_set, support_data[frequent_set])
	print()
	print("Rules")
	for item in rule_list:
		print(item[0], "=>", item[1], "'s conf: ", item[2])
 

命令列執行結果：

自己蛋疼畫的圖，沒看到人家大佬已經畫了...

這些東西最近就不學了，等一些東西塵埃落定之後再說吧...

繼續加油~