Disk Allocation Methods Using Error Correcting Codes
IEEE Transactions on Computers
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A spatial data mining method by Delaunay triangulation
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Disk allocation for Cartesian product files on multiple-disk systems
ACM Transactions on Database Systems (TODS)
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
PDIS '93 Proceedings of the second international conference on Parallel and distributed information systems
Scalability Analysis of Declustering Methods for Multidimensional Range Queries
IEEE Transactions on Knowledge and Data Engineering
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Cyclic Allocation of Two-Dimensional Data
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Optimal Allocation of Two-Dimensional Data
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The Idea of De-Clustering and its Applications
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Declustering Using Golden Ratio Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
IEEE Transactions on Knowledge and Data Engineering
A study on grid partition for declustering high-dimensional data
ADVIS'04 Proceedings of the Third international conference on Advances in Information Systems
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In this paper, we propose an efficient declustering algorithm which is adaptable in different data distribution. Previous declustering algorithms have a potential drawback by assuming data distribution is uniform. However, our method shows a good declustering performance for spatial data regardless of data distribution by taking it into consideration. First, we apply a spatial clustering algorithm to find the distribution in the underlying data and then allocate a disk page to each unit of cluster. Second, we analyize the effect of outliers on the performance of declustering algorithm and propose to handle them separately. Experimental results show that these approaches outperform traditional declustering algorithms based on tiling and mapping function such as DM, FX, HCAM and Golden Ratio Sequence.