关联规则挖掘和关联规则挖掘有什么区别频繁项集挖掘
我是数据挖掘新手，对关联规则和频繁项挖掘感到困惑。对我来说，我认为两者是相同的，但我需要这个论坛上专家的意见
我的问题是
关联规则挖掘和关联规则挖掘之间有什么区别？频繁项集挖掘？ 谢谢
i am new to data mining and confuse about Association rules and frequent item mining. for me i think both are same but i need views from experts on this forum
My question is
what is the difference between Association rule mining & frequent itemset mining?
Thanks
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关联规则类似于“A，B → C”，意味着当 A 和 B 出现时，C 往往会出现。项目集只是一个集合，例如“A，B，C”，如果它的项目倾向于同时出现，那么它就是频繁的。 查找关联规则的通常方法是查找所有频繁项集，然后将它们后处理为规则。
An association rule is something like "A,B → C", meaning that C tends to occur when A and B occur. An itemset is just a collection such as "A,B,C", and it is frequent if its items tend to co-occur. The usual way to look for association rules is to find all frequent itemsets and then postprocess them into rules.
频繁项集挖掘的输入是：
输出是：
关联规则挖掘的输入是：
输出是：
现在项集和关联规则挖掘之间的关系是，使用频繁项集来生成规则是非常有效的（参见 Agrawal 1993 的论文）以获取有关此想法的更多详细信息。因此关联规则挖掘将分为两个步骤：
- 挖掘频繁项集
- 使用频繁项集生成所有有效的关联规则。
The input of frequent itemset mining is :
The output is :
The input of assocition rule mining is :
The output is :
Now the relationship between itemset and association rule mining is that it is very efficient to use the frequent itemset to generate rules (see the paper by Agrawal 1993) for more details about this idea. So association rule mining will be broken down into two steps:
- mining frequent itemsets
- generating all valid association rules by using the frequent itemsets.
频繁项集挖掘是关联规则挖掘的第一步。
一旦生成了所有的频繁项集，就可以对它们进行逐一迭代，枚举所有可能的关联规则，计算它们的置信度，最后，如果置信度>1，则计算出它们的置信度。
minConfidence，您输出该规则。Frequent itemset mining is the first step of Association rule mining.
Once you have generated all the frequent itemsets, you proceed by iterating over them, one by one, enumerating through all the possible association rules, calculate their confidence, finally, if the confidence is >
minConfidence, you output that rule.频繁项集挖掘是关联规则挖掘的一个步骤。对数据应用 Apriori、FPGrowth 等频繁项集挖掘算法后，您将得到频繁项集。从这些
发现频繁项集，您将生成关联规则（通常通过子集生成完成）。
Frequent itemset mining is a step of Association rules mining. After applying Frequent itemset mining algorithm like Apriori, FPGrowth on data, you will get frequent itemsets. From these
discovered frequent itemsets, you will generate association rules(Usually done by subset generation).
通过使用关联规则挖掘，我们将获得给定数据集中出现的频繁项集。它还提供了不同类型的算法来挖掘频繁项集，但以不同的方式完成，即水平或垂直格式。 Apriori算法遵循水平格式来挖掘频繁项集，而eclat算法遵循垂直格式来挖掘频繁数据集。
By using Association rule mining we will get the frequently itemsets that present in the given dataset. it also provide different types of algorithms for mining the frequent itemsets but it is done in different way that means either horizontal or vertical format. Apriori algorithm follow the horizontal format for mining the frequent itemsets and eclat algorithm follow the vertical format for mining the frequent datasets.
关联规则挖掘：
关联规则挖掘用于发现数据中的模式。它发现一起出现且相关的特征。
例如，购买尿布的人可能会购买婴儿爽身粉。或者我们可以将这个陈述改写为：如果（人们购买尿布），那么（他们购买婴儿爽身粉）。注意 if,then 规则。这并不一定意味着如果人们购买婴儿爽身粉，他们就会购买尿布。一般来说，我们可以说，如果条件A趋向于B，并不一定意味着B趋向于A。
频繁项集挖掘：
频繁项集挖掘用于发现数据中的公共项集。它可以从给定的事务数据集生成关联规则。
如果经常购买 2 件商品 X 和 Y，那么最好将它们放在商店中，或者在购买另一件商品时为其中一件商品提供一些折扣优惠。这样确实可以增加销量。例如，可能会发现，如果顾客购买牛奶和面包，他/她也会购买黄油。
所以关联规则是['牛奶]^['面包']=>['黄油']。因此，如果顾客购买牛奶和面包，卖家可以建议顾客购买黄油。
Association Rule mining:
Association rule mining is used to find the patterns in data.it finds the features which occur together and correlated.
For example, people who buy diapers are likely to buy baby powder. Or we can rephrase the statement by saying: If (people buy diaper), then (they buy baby powder). Note the if, then rule. This does not necessarily mean that if people buy baby powder, they buy diaper. In General, we can say that if condition A tends to B it does not necessarily mean that B tends to A.
Frequent item set mining:
Frequent item set mining is used to find the common item sets in data. it can generate association rules from the given transactional datasets.
If there are 2 items X and Y purchased frequently then its good to put them together in stores or provide some discount offer on one item on purchase of other item. This can really increase the sales. For example it is likely to find that if a customer buys Milk and bread he/she also buys Butter.
So the association rule is [‘milk]^[‘bread’]=>[‘butter’]. So seller can suggest the customer to buy butter if he/she buys Milk and Bread.