Iceberg-cube algorithms: An empirical evaluation on synthetic and real data

Abstract

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.