Difference between apriori and fp growth algorithm
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Apriori and FPGrowth are two algorithms for frequent itemset mining. They have the same input and the same output. The input is a transaction database and a minimum support threshold. The output is the set of itemsets having a support no less than the minimum support threshold.
So what is the difference between these algorithms then? The difference between these algorithms is how they generate the output. The result is the same, but the process to obtain the result is different. Briefly, Apriori utilize a level-wise approach where it will generate patterns containing 1 items, then 2 items, 3 items, etc. Moreover, it will repeatedly scan the database to count the support of each pattern. On the other hand, FPGrowth utilizes a depth-first search instead of a breadth first search and uses a pattern-growth approach (this means that unlike Apriori, it only considers patterns actually existing in the database. This is just a brief explanation. You can read my survey paper about itemset mining, which explains all these ideas in more details and give you an overview of the field
So what is the difference between these algorithms then? The difference between these algorithms is how they generate the output. The result is the same, but the process to obtain the result is different. Briefly, Apriori utilize a level-wise approach where it will generate patterns containing 1 items, then 2 items, 3 items, etc. Moreover, it will repeatedly scan the database to count the support of each pattern. On the other hand, FPGrowth utilizes a depth-first search instead of a breadth first search and uses a pattern-growth approach (this means that unlike Apriori, it only considers patterns actually existing in the database. This is just a brief explanation. You can read my survey paper about itemset mining, which explains all these ideas in more details and give you an overview of the field
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