Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Quasi-cubes: exploiting approximations in multidimensional databases
ACM SIGMOD Record
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Modern Information Retrieval
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
High-dimensional OLAP: a minimal cubing approach
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Time-HOBI: Index for optimizing star queries
Information Systems
| Hi-index | 0.00 |
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.