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

  • Xiaofeng CaoEmail author
  • Baozhi Qiu
  • Guandong Xu
Theoretical Advances


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.


Cluster boundary MeanShift Parzen window High-dimensional space 


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information EngineeringZhengzhou UniversityZhengzhouChina
  2. 2.Advanced Analytics InstituteUniversity of Technology SydneySydneyAustralia

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