Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
VLDB '89 Proceedings of the 15th international conference on Very large data bases
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Towards an analysis of range query performance in spatial data structures
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Data & Knowledge Engineering
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Density biased sampling: an improved method for data mining and clustering
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data bubbles: quality preserving performance boosting for hierarchical clustering
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Modeling high-dimensional index structures using sampling
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules
Data Mining and Knowledge Discovery
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Sampling from Spatial Databases
Proceedings of the Ninth International Conference on Data Engineering
Similarity-Driven Sampling for Data Mining
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
C2P: Clustering based on Closest Pairs
Proceedings of the 27th International Conference on Very Large Data Bases
Random Sampling from Pseudo-Ranked B+ Trees
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Evaluation of sampling for data mining of association rules
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets
IEEE Transactions on Knowledge and Data Engineering
Feature-preserved sampling over streaming data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Weighted k-means for density-biased clustering
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
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In this paper we describe a new density-biased sampling algorithm. It exploits spatial indexes and the local density information they preserve, to provide improved quality of sampling result and fast access to elements of the dataset. It attains improved sampling quality, with respect to factors like skew, noise or dimensionality. Moreover, it has the advantage of efficiently handling dynamic updates, and it requires low execution times. The performance of the proposed method is examined experimentally. The comparative results illustrate its superiority over existing methods.