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.KeywordsFrequent PatternData CubeInverted IndexPruning StrategyLarge CardinalityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.