Mining frequent closed cubes in 3D datasets

Abstract

In this paper, we introduce the concept of frequent closed cube (FCC), which generalizes the notion of 2D frequent closed pattern to 3D context. We propose two novel algorithms to mine FCCs from 3D datasets. The first scheme is a Representative Slice Mining (RSM) framework that can be used to extend existing 2D FCP mining algorithms for FCC mining. The second technique, called CubeMiner, is a novel algorithm that operates on the 3D space directly. We have implemented both schemes, and evaluated their performance on both real and synthetic datasets. The experimental results show that the RSM-based scheme is efficient when one of the dimensions is small, while CubeMiner is superior otherwise.