IMPLEMENTATION OF ARRAY BASED TECHNIQUE TO IMPROVISE REPRESENTATION OF FP-TREE USING IAFP-MAX ALGORITHM

Abstract

The essential aspect of mining association rules is to mine the frequent patterns. Due to intrinsic difficulty it is impossible to mine complete frequent patterns from a dense database. The quantity of mined patterns is generally large and it is firm to understand and utilize them. all frequent patterns are enclosed and compressed to maximal frequent patterns where the memory needed for storing them is smaller than that is required for storing complete patterns. Consequently, mining maximal frequent patterns provides a great value. This paper inorder to improve the structure of traditional FP-Tree presents an effective algorithm called IAFP-max for mining maximal frequent patterns based on improved FP-tree and array technique. The implementation of concept postfix sub- tree in the respective algorithm avoids generating the candidate of maximal frequent patterns in the mining process. Thus it reduces the memory consumed and also uses an array –based technique to the improved FP-Tree to reduce the traverse time. By the practical facts ,it represents that this algorithm overtakes many existing algorithms like MAFIA, Genax and FP max.

Keywords: FP-Tree, IAFP, Frequent Patterns, itemsets, Array Technique.