An introduction to data structures with applications (2nd ed.)
An introduction to data structures with applications (2nd ed.)
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
An Empirical Comparison of Methods for Iceberg-CUBE Construction
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
The Multi-Tree Cubing algorithm for computing iceberg cubes
Journal of Intelligent Information Systems
Finding an application-appropriate model for XML data warehouses
Information Systems
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The Iceberg-Cube problem is to identify the combinations of values for a set of attributes for which a specified aggregation function yields values over a specified aggregate threshold. We implemented bottom-up and top-down methods for this problem and performed extensive experiments featuring a variety of synthetic and real databases. The bottom-up method included pruning. Results show that in most cases the top-down method, with or without pruning, was slower than the bottom-up method, because of less effective pruning. However, below a crossover point, the top-down method is faster. This crossover point occurs at a relatively low minimum support threshold, such as 0.01% or 1.5%. The bottom-up method is recommended for cases when a minimum support threshold higher than the crossover point will be selected. The top-down method is recommended when a minimum support threshold lower than the crossover point will be used or when a large number of results is expected.