A linear-time probabilistic counting algorithm for database applications
ACM Transactions on Database Systems (TODS)
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Multimedia database management systems
ACM Computing Surveys (CSUR)
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 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
Multimedia database management (panel session)
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th 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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Client-Server Architecture for Accessing Multimedia and Geographic Databases within Embedded Systems
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
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Iceberg queries are to compute aggregate functions over an attribute (or set of attributes) to find aggregate values above some specified threshold. It's difficult to execute these queries because the number of unique data is greater than the number of counter buckets in memory. However, previous research has the limitation that average functions were out of consideration among aggregate functions. So, in order to compute average iceberg queries efficiently we introduce the theorem to select candidates by means of partitioning, and propose POP algorithm based on it. The characteristics of this algorithm are to partition a relation logically and to postpone partitioning to use memory efficiently until all buckets are occupied with candidates. Experiments show that proposed algorithm is affected by memory size, data order, and the distribution of data set.