BorderShift: Toward optimal MeanShift vector for cluster boundary detection in high-dimensional data

Xiaofeng CAO, Baozhi QIU, Guandong XU

Research output: Contribution to journalArticlespeer-review

9 Citations (Scopus)

Abstract

We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms. Copyright © 2018 Springer-Verlag London Ltd., part of Springer Nature.

Original languageEnglish
Pages (from-to)1015-1027
JournalPattern Analysis and Applications
Volume22
Early online dateMay 2018
DOIs
Publication statusPublished - Aug 2019

Citation

Cao, X., Qiu, B., & Xu, G. (2019). BorderShift: Toward optimal MeanShift vector for cluster boundary detection in high-dimensional data. Pattern Analysis and Applications, 22, 1015-1027. https://doi.org/10.1007/s10044-018-0709-0

Keywords

  • Cluster boundary
  • MeanShift
  • Parzen window
  • High-dimensional space

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