BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data
Pattern Analysis and Applications(2018)
摘要
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 k NN 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.
更多查看译文
关键词
Cluster boundary,MeanShift,Parzen window,High-dimensional space
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络