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 the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn't need multiiteration to get the optimal detection result. Instead, the developed Shif t 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 Fmeasure evaluation of BorderShift is higher than that of other algorithms.
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