A study on FP Tree Algorithms for Association Rules

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A. Naresh
Prof Geetha Mary. A


A study on the performance of the FP growth method shows that it is efficient and scalable for mining both long and short frequent
patterns, and is about an order of magnitude faster than Apriori algorithm. The frequent pattern tree structure, which is an extended prefix tree
structure for storing compressed, crucial information about frequent pattern and develop an efficient FP-tree, based mining method. Efficiency of
mining is achieved with three techniques: (1) a large database is compressed into a smaller data structure; FP-tree avoids costly, repeated
database scans, (2) our FP-tree-based mining avoids the costly generation of a large number of candidate sets, and (3) The FP-tree algorithm
avoid repeated scan of database and search database in divide-and-conquer based. We also perform an evaluation study of the hiding algorithms
in order to analyze their time complexity and the impact that they have in the original database.



Keywords: Apriori algorithm, FP-tree algorithm, Frequent Pattern, FP-growth


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