Mining Frequent Itemsets using Temporal Association Rule

Abstract

Association rule mining is to find association relationships among large data sets. Mining frequent patterns is an important aspect in association rule mining. Most of the popular associationship rule mining methods are having performance bottleneck for database with different characteristics of data such as dense vs. sparse. In this paper, an efficient algorithm named Temporal FP-Tree (Frequent Pattern - Tree) algorithm and the FP-tree structure is presented to mine frequent patterns, conditional pattern bases and sub- conditional pattern tree recursively .This algorithm is used to mine frequent patterns from temporal database and it needs limited memory space. When dataset becomes dense it can be scaled up to large database by partitioning it, conditionally temporal FP-tree can be constructed dynamically as part of mining.