Description: This Program Shows How To Create A Simple Market Basket Analysis In C++ Using rand() function to generate item type and item quantity Platform: |
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Author:deni |
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Description: 关联规则的目的在于在一个数据集中找出项之间的关系，也称之为购物蓝分析 (market basket analysis)。Apriori algorithm是关联规则里一项基本算法。-The purpose of association rules in a data set to identify relationships between items, also known as shopping blue analysis (market basket analysis). Apriori algorithm is a fundamental algorithm in association rule. Platform: |
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Author:段保京 |
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Description: Apriori[1] is a classic algorithm for frequent itemset mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. Platform: |
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Author:kfnei.dk |
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Description: There are many ways to see the similarities between items. These are techniques that fall under the general umbrella of association. The outcome of this type of technique, in simple terms, is a set of rules that can be understood as “if this, then that”.
Imagine 10000 receipts sitting on your table. Each receipt represents a transaction with items that were purchased. The receipt is a representation of stuff that went into a customer’s basket – and therefore ‘Market Basket Analysis’.
That is exactly what the Groceries Data Set contains: a collection of receipts with each line representing 1 receipt and the items purchased. Each line is called a transaction and each column in a row represents an item. Platform: |
Size: 19456 |
Author:tenaj |
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Description: Apriori[1] is an algorithm for frequent item set mining and association rule learning over transactional databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database. The frequent item sets determined by Apriori can be used to determine association rules which highlight general trends in the database: this has applications in domains such as market basket analysis. Platform: |
Size: 666624 |
Author:sensensen |
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