A new approach for maximizing bichromatic reverse nearest neighbor search

Maximizing bichromatic reverse nearest neighbor (MaxBRNN) is a variant of bichromatic reverse nearest neighbor (BRNN). The purpose of the MaxBRNN problem is to find an optimal region that maximizes the size of BRNNs. This problem has lots of real applications such as location planning and profile-ba...

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Bibliographic Details
Published in:Knowledge and information systems 2013-07, Vol.36 (1), p.23-58
Main Authors: Liu, Yubao, Wong, Raymond Chi-Wing, Wang, Ke, Li, Zhijie, Chen, Cheng, Chen, Zhitong
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Computer Science
Computer science
control theory
systems
Convenience stores
Data Mining and Knowledge Discovery
Database Management
Euclidean space
Exact sciences and technology
Experiments
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
Information systems. Data bases
IT in Business
Marketing
Memory organisation. Data processing
Metric space
Optimization
Regular Paper
Searches
Searching
Software
Studies
Three dimensional
Two dimensional
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Summary:Maximizing bichromatic reverse nearest neighbor (MaxBRNN) is a variant of bichromatic reverse nearest neighbor (BRNN). The purpose of the MaxBRNN problem is to find an optimal region that maximizes the size of BRNNs. This problem has lots of real applications such as location planning and profile-based marketing. The best-known algorithm for the MaxBRNN problem is called MaxOverlap . In this paper, we study the MaxBRNN problem and propose a new approach called MaxSegment for a two-dimensional space when the -norm is used. Then, we extend our algorithm to other variations of the MaxBRNN problem such as the MaxBRNN problem with other metric spaces, and a three-dimensional space. Finally, we conducted experiments on real and synthetic datasets to compare our proposed algorithm with existing algorithms. The experimental results verify the efficiency of our proposed approach.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-012-0527-4