pcy 算法
PCY algorithm was developed by three Chinese scientists Park, Chen, and Yu. This is an algorithm used in the field of big data analytics for the frequent itemset mining when the dataset is very large.
PCY算法是由三位中国科学家Park，Chen和Yu开发的。 当数据集非常大时，这是在大数据分析领域中用于频繁项集挖掘的算法。
Consider we have a huge collection of data, and in this data, we have a number of transactions. For example, if we buy any product online it’s transaction is being noted. Let, a person is buying a shirt from any site now, along with the shirt the site advised the person to buy jeans also, with some discount. So, we can see that how two different things are made into a single set and associated. The main purpose of this algorithm is to make frequent item sets say, along with shirt people frequently buy jeans.
考虑一下我们有大量的数据收集，并且在这些数据中，我们有许多事务。 例如，如果我们在线购买任何产品，则会记录其交易。 现在，一个人正在从任何站点购买衬衫，该站点建议该人也购买衬衫，并有一定折扣。 因此，我们可以看到如何将两个不同的东西组合成一个单一的集合。 该算法的主要目的是使频繁的服装搭配，以及人们经常购买的衬衫。
For example:
例如：
Transaction Items bought Transaction 1 Shirt + jeans Transaction 2 Shirt + jeans +Trouser Transaction 3 Shirt +Tie Transaction 4 Shirt +jeans +shoes So, from the above example we can see that shirt is most frequently bought along with jeans, so, it is considered as a frequent itemset.
因此，从上面的示例中，我们可以看到衬衫与牛仔裤一起最常购买，因此，它被认为是一件频繁的商品。
An example problem solved using PCY algorithm
使用PCY算法解决的示例问题
Question: Apply PCY algorithm on the following transaction to find the candidate sets (frequent sets).
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