NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms

Efficient search for nearest neighbors (NN) is a fundamental problem arising in a large variety of applications of vast practical interest. In this paper we propose a novel technique, called NNH ("Nearest Neighbor Histograms"), which uses specific histogram structures to improve the performance of NN search algorithms. A primary feature of our proposal is that such histogram structures can co-exist in conjunction with a plethora of NN search algorithms without the need to substantially modify them. The main idea behind our proposal is to choose a small number of pivot objects in the space, and pre-calculate the distances to their nearest neighbors. We provide a complete specification of such histogram structures and show how to use the information they provide towards more effective searching. In particular, we show how to construct them, how to decide the number of pivots, how to choose pivot objects, how to incrementally maintain them under dynamic updates, and how to utili...

• Type: Conference
• Year: 2004
• Where: EDBT
• Authors: Liang Jin, Nick Koudas, Chen Li
• Keywords: Database, EDBT 2004, Nearest Neighbor Histograms, NN Search Algorithms, NN Searches