In this paper we propose a new dynamic Metric Access Method (MAM) called DBM*-Tree, which uses precomputed distances to reduce the construction cost avoiding repeated calculus of distance. Making use of the pre-calculated distances cost of similarity queries are also reduced by taking various local representative objects in order to increment the pruning of irrelevant elements during the query. We also propose a new algorithm to select the suitable subtree in the insertion operation, which is an evolution of the previous methods. Empiric tests on real and synthetic data have shown evidence that DBM*-Tree requires 25 % less average distance computing than Density Based Metric -Tree (DBM-Tree) which is one of the most efficient and recent MAM found in the literature.
We propose a minimum overlap based hyperspherical region graph indexing structure to achieve fast similaritybased queries for both low and high dimensional datasets. Specifically, we reduce the region overlaps in the graph construction phase by incrementally dividing each saturated hyperspherical region and removing the longest edge of a minimum spanning tree representation of the internal objects. This overlap reduction scheme creates more separated regions, so fewer regions as potential paths are traversed when a query is issued. We also introduce a knearest-neighbor search scheme by automatically deciding the search radius to return the required number of nearest neighbors. Our extensive experimental results show the effectiveness of the proposed indexing structure compared with other tree and graph based indexing structures.
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