Extended K-d Tree Database Organization: A Dynamic Multiattribute Clustering Method

Abstract

The problem of performing multiple attribute clustering in a dynamic database is studied. The extended K-d tree method is presented. In an extended K-d tree organization, the basic k-d tree structure after modification is used as the structure of the directory which organizes the data records in the secondary storage. The discriminator value of each level of the directory determines the partitioning direction of the corresponding attribute subspace. When the record insertion causes the data page to overload, the attribute space will be further partitioned along the direction specified by the corresponding discriminator.