MOBHRG: Fast k-nearest-neighbor search by overlap reduction of hyperspherical regions

We propose a minimum overlap based hyperspherical region graph indexing structure to achieve fast similarity-based 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 k-nearest-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.