MK-tree: An Effective Access Method for Indexing High Dimensional Data

Guoren Wang; Xiangmin Zhou; Bin Wang; Baiyou Qiao; Donghong Han
December 2005
Journal of Digital Information Management;Dec2005, Vol. 3 Issue 4, p257
Academic Journal
In this paper, we propose an efficient access method, named MK-tree, to dynamically index large data sets in high dimensional spaces. It is an extension of M-tree with key dimension to improve the efficiency of space partition and reduce the response time of similarity search for high dimensional data. The main idea behind the key dimension is to make the fanout of tree larger by partitioning a subspace further into two subspaces, called a twin-node, according to the key dimension. To get a high space utilization, we conduct data reallocation within a twin-node dynamically, therefore further improve the performance of MK-tree. Our experimental results show that a higher filtering efficiency can be obtained by using the concept of key dimension for both R-neighbor search and K-nearest neighbor search.


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