Exploring bit-difference for approximate KNN search in high-dimensional databases - Cui, Bin (Institutional Knowledge at Singapore Management University, 2005)
In this paper, we develop a novel index structure to support effcient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID+. Extensive experiments are conducted to show that our proposed method yields signifcant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.