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- W2049416518 abstract "In this study, a technique of performing multiple attribute clustering in dynamic databases has been investigated. We have transformed the problem of performing multiple attribute clustering into a problem of dynamically partitioning the attribute space. The optimal number of partitioning of the attribute space in a dynamic database environment has been analyzed, the partitioning direction is controlled by a discriminator sequence. The design of the discriminator sequence to obtain the optimal partitioning is presented. The selection of the directory attributes has also been discussed. Using the extended K-d tree to direct the partitioning, we have presented the extended K-d tree method. Empirical results have justified the improvement of the performance using the extended K-d tree method, when compared with that using the single attribute clustering or using inverted file method without any clustering index." @default.
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- W2049416518 date "1980-01-01" @default.
- W2049416518 modified "2023-10-16" @default.
- W2049416518 title "A dynamic clustering technique for physical database design" @default.
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- W2049416518 doi "https://doi.org/10.1145/582250.582280" @default.
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