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  • FP Growth

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Affinity Analysis, also known as Market Basket Analysis, is a modelling technique based on the premise that if you buy one group of things, you're more likely to buy another. Someone who buys peanut butter and bread, for example, is far more likely to want to buy jelly as well.

The Apriori Algorithm is a well-known Association Rule algorithm that is frequently used in market basket analysis. It is also thought to be more accurate than the AIS and SETM algorithms. It aids in the discovery of frequent itemsets in transactions as well as the identification of association rules between these items.

The database is represented by the FP growth method as a tree termed a frequent pattern tree or FP tree. The relationship between the itemsets will be maintained by this tree structure. Using one common object, the database is fractured. The fragmented section is referred to as a "pattern fragment."

The first strategy is Mine Merge, which does not require a specific mining algorithm and can be applied to FP-growth without any changes. The second is an FP-growth implementation of the general concept of Common Counting.

Internally, FP-growth is a method that does not necessitate the development of candidates. It employs an FP-tree data structure that eliminates the need for explicit candidate set construction, making the approach more suitable for big databases. The algorithm of frequent sets is discovered using the FP-tree.

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