A novel approach for mining frequent patterns from incremental data

Abstract

Incremental data can be defined as dynamic data that changes as time advances. Mining frequent patterns from such data is costly as most of the approaches need repetitive scanning and generates a large number of candidate keys. It is important to develop an efficient approach to enhance the performance of mining. This paper proposes a novel tree-based data structure for mining frequent pattern of incremental data called Tree for Incremental Mining of Frequent Pattern (TIMFP) which is compact as well as almost balanced. TIMFP is also suitable for interactive mining (build once and mine many). We have compared TIMFP with canonical-order tree (CanTree), Compressed and Arranged Transaction Sequences (CATS) Tree and Incremental Mining Binary Tree (IMBT). The experimental results show that the proposed work has better performance than other data structures compared in the paper in terms of time required for constructing the tree as well as mining frequent patterns from the tree.