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Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases

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dc.creator Cui, Bin
dc.creator Shen, Heng Tao
dc.creator Shen, Jialie
dc.creator Tan, Kian Lee
dc.date 2004-12-13T05:57:15Z
dc.date 2004-12-13T05:57:15Z
dc.date 2005-01
dc.date.accessioned 2013-10-09T02:49:27Z
dc.date.available 2013-10-09T02:49:27Z
dc.date.issued 2013-10-09
dc.identifier http://hdl.handle.net/1721.1/7416
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description 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.
dc.description Singapore-MIT Alliance (SMA)
dc.format 150433 bytes
dc.format application/pdf
dc.language en
dc.relation Computer Science (CS);
dc.subject High-dimensional index structure
dc.subject bit difference
dc.subject approximate KNN query
dc.subject memory processing
dc.subject BID+
dc.title Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases
dc.type Article


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