Article

BRIM: an efficient boundary points detecting algorithm

Authors:

Bao-Zhi Qiu,

Feng Yue,

Jun-Yi ShenAuthors Info & Claims

PAKDD'07: Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining

Pages 761 - 768

Published: 22 May 2007 Publication History

Abstract

In order to detect boundary points of clusters effectively, we propose a technique making use of a point's distribution feature of its Eps neighborhood to detect boundary points, and develop a boundary points detecting algorithm BRIM (an efficient Boundary points detecting algorithm). Experimental results show that BRIM can detect boundary points in noisy datasets containing clusters of different shapes and sizes effectively and efficiently.

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Chenyi Xia, Wynne Hsu, Mong Li Lee etal. BODER:Efficient Computation of Boundary Points. IEEE transaction on knowledge and data engineering, 2006, 18(3):289-303.

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Cited By

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Cao XQiu BXu G(2019)BorderShiftPattern Analysis & Applications10.1007/s10044-018-0709-022:3(1015-1027)Online publication date: 31-Jul-2019
https://dl.acm.org/doi/10.1007/s10044-018-0709-0
Tong QLi XYuan B(2018)Efficient distributed clustering using boundary informationNeurocomputing10.1016/j.neucom.2017.11.014275:C(2355-2366)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.11.014
Li XHan QQiu B(2018)A clustering algorithm with affine space-based boundary detectionApplied Intelligence10.1007/s10489-017-0979-z48:2(432-444)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s10489-017-0979-z
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BRIM: an efficient boundary points detecting algorithm
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
2. Information systems
  1. Information systems applications

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Published In

PAKDD'07: Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining

May 2007

1160 pages

ISBN:9783540717003

Editors:
Zhi-Hua Zhou
Nanjing University, National Lab for Novel Software Technology, Nanjing, China
,
Hang Li
Microsoft Research Asia, Beijing, China
,
Qiang Yang
Hong Kong University of Science and Technology, Department of Computer Science and Engineering, Clearwater Bay, Kowloon, Hong Kong, China

In-Cooperation

Singapore Institute of Statistics
Nanjing University of Aeronautics and Astronautics
The Japanese Society for Artificial Intelligence

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 22 May 2007

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Cao XQiu BXu G(2019)BorderShiftPattern Analysis & Applications10.1007/s10044-018-0709-022:3(1015-1027)Online publication date: 31-Jul-2019
https://dl.acm.org/doi/10.1007/s10044-018-0709-0
Tong QLi XYuan B(2018)Efficient distributed clustering using boundary informationNeurocomputing10.1016/j.neucom.2017.11.014275:C(2355-2366)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.11.014
Li XHan QQiu B(2018)A clustering algorithm with affine space-based boundary detectionApplied Intelligence10.1007/s10489-017-0979-z48:2(432-444)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s10489-017-0979-z
(2017)A highly scalable clustering scheme using boundary informationPattern Recognition Letters10.1016/j.patrec.2017.01.01689:C(1-7)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.patrec.2017.01.016

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A Boundary Detection algorithm of clusters based on Dual Threshold Segmentation

Unifying Density-Based Clustering and Outlier Detection

BORDER: Efficient Computation of Boundary Points

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