FP Growth(FP-tree) Algorithm with Example

FP-growth algorithm : Introduction The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. The FP-Growth methods adopts a divide and conquer strategy as follows: compress the database representing frequent items into a frequent-pattern tree, but retain the itemset association information, and then divide such a compressed database into a set of condition databases, each associated with one frequent item, and mine each such database. First, a scan of database derives a list of frequent items in descending order. Then the FP – tree is constructed as follows. Create the root of the tree and scan the database second time. The items in each transaction are processed in the order of frequent items list and a branch is created for each transaction. When considering the branch to be added to a transaction, the count of each node along a common prefix is incremented by 1. After constructing the tree the mining proceeds as follows. Start from each frequent length-1 pattern, construct its conditional pattern base, then construct its conditional FP-tree and perform mining recursively on such a tree. The support of a candidate (conditional) itemset is counted traversing the tree. The sum of count values…

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