Mining Correlated Pairs of Patterns in Multidimensional Structured Databases

Abstract

Structured data is becoming increasingly abundant in many application domains recently. In this paper, as one of the correlation mining, we propose new data mining problems of finding frequent and correlated pairs of patterns in structured databases. First, we consider the problem of finding all frequent and correlated pattern pairs in two dimensional structured databases. Then, two kinds of top-k mining problems are studied. To solve these problems efficiently, we develop a series of algorithms having powerful pruning capabilities. We also discuss the applicability of the proposed algorithms to the discovery of pattern pairs in single and multidimensional structured databases. The effectiveness of proposed algorithms is assessed through the experiments with synthetic and real world datasets.