Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/40951
Title: ERkNN: Efficient Reverse k-Nearest Neighbors retrieval with local kNN-distance estimation
Authors: Xia, C.
Hsu, W. 
Lee, M.L. 
Keywords: KNN distance estimation
Reverse k-nearest neighbours
Issue Date: 2005
Source: Xia, C.,Hsu, W.,Lee, M.L. (2005). ERkNN: Efficient Reverse k-Nearest Neighbors retrieval with local kNN-distance estimation. International Conference on Information and Knowledge Management, Proceedings : 533-540. ScholarBank@NUS Repository.
Abstract: The Reverse k-Nearest Neighbors (RkNN) queries are important in profile-based marketing, information retrieval, decision support and data mining systems. However, they are very expensive and existing algorithms are not scalable to queries in high dimensional spaces or of large values of k. This paper describes an efficient estimation-based RkNN search algorithm (ERkNN) which answers RkNN queries based on local kNN-distance estimation methods. The proposed approach utilizes estimation-based filtering strategy to lower the computation cost of RkNN queries. The results of extensive experiments on both synthetic and real life datasets demonstrate that ERkNN algorithm retrieves RkNN efficiently and is scalable with respect to data dimensionality, k, and data size. Copyright 2005 ACM.
Source Title: International Conference on Information and Knowledge Management, Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/40951
ISBN: 1595931406
Appears in Collections:Staff Publications

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