Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases
Author(s)
Cui, Bin
Shen, Heng Tao
Shen, Jialie
Tan, Kian Lee
Date Issued
January 2005
Series/Report no.
Computer Science (CS);
Abstract
In this paper, we develop a novel index structure to support efficient 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 significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.
Subjects
High-dimensional index structure
bit difference
approximate KNN query
memory processing
BID+
Persistent DSpace Link
