Mining Multi-dimensional Frequent Patterns Without Data Cube Construction

Abstract

Existing approaches for multi-dimensional frequent patterns mining rely on the construction of data cube. Since the space of a data cube grows explosively as dimensionality or cardinality grows, it is too costly to materialize a full data cube, esp. when dimensionality or cardinality is large. In this paper, an efficient method is proposed to mine multi-dimensional frequent patterns without data cube construction. The main contributions include: (1) formally proposing the concept of multi-dimensional frequent pattern and its pruning strategy based on Extended Apriori Property, (2) proposing a novel structure called Multi-dimensional Index Tree (MDIT) and a MDIT-based multi-dimensional frequent patterns mining method (MDIT-Mining), and (3) conducting extensive experiments which show that the space consuming of MDIT is more than 4 orders of multitudes smaller than that of data cube along with the increasing of dimensionality or cardinality at most cases.

This work was supported by National Science Foundation of China (60473071), Specialized Research Fund for Doctoral Program by the Ministry of Education (SRFDP 20020610007), the grant from the State Administration of Traditional Chinese Medicine (SATCM 2003JP40) and National Science Foundation of China (90409007). Chuan Li, Tianqing Zhang, Yintian Liu, Qihong Liu, and Mingfang Zhu are Ph. D Candidates at DB&KE Lab, Sichuan University. YU Zhonghua is a professor at Sichuan University. JIANG Yongguang is a professor at Chengdu University of TCM. And TANG Changjie is the associate author.