An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Supporting Imprecision in Multidimensional Databases Using Granularities
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
CURE for cubes: cubing using a ROLAP engine
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mining frequent closed cubes in 3D datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Enhanced mining of association rules from data cubes
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Data Mining and Knowledge Discovery
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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It is well recognized that data cubes often produce huge outputs. Several efforts were devoted to this problem through closed cubes, where cells preserving aggregation semantics are losslessly reduced to one cell. In this paper, we introduce the concept of closed non derivable data cube , denoted $\mathcal{CND}$ - $\mathcal{C}$ube, which generalizes the notion of bi-dimensional frequent closed non derivable patterns to the multidimensional context. We propose a novel algorithm to mine $\mathcal{CND}$ - $\mathcal{C}$ube from multidimensional databases considering three anti-monotone constraints, namely "to be frequent ", "to be non derivable " and "to be minimal generator ". Experiments show that our proposal provides the smallest representation of a data cube and thus is the most efficient for saving storage space.