BRINK: An Algorithm of Boundary Points of Clusters Detecton Based On Local Qualitative Factors
Xiaowei Du · Journal of Zhengzhou University · 2012
In order to detect boundary points of clusters efficiently,we present an algorithm of boundary points detection based on local qualitative factors(BRINK).This algorithm uses weighted euclidean distance to solve high dimensional data problem which most of the existing clusters detecting algorithms can not deal with.According to the feature of local qualitative factors,the individual finds that it is lightly larger than 1 in boundary points of clusters.we can detect the boundary points with the former two processes.As shown by the experimental results,BRINK can detect boundary points in noisy high-dimensional datasets containing clusters of arbitrary shapes,sizes and different densities.