A novel clustering oriented closeness measure based on neighborhood chain
Shaoyi Liang, Deqiang Han, Lei Zhang, Qinke Peng · 2017
Closeness measures are crucial to clustering methods. In most traditional clustering methods, the closeness between data points or clusters is measured by the geometric distances alone. These metrics quantify the closeness only based on the concerned data points' positions in the feature space, and they might cause problems when dealing with clustering tasks with arbitrary clusters shapes and different clusters scales (varying clusters densities). In this paper, a novel Closeness Measure between data points based on Neighborhood Chain (CMNC) is proposed. Instead of using geometric distances alone, CMNC measures the closeness between data points by quantifying the difficulty for one data point to reach another through a chain of neighbors. Experimental results show that by substituting the geometric-distances-based closeness measures with CMNC, modified versions of the traditional clustering algorithms (e.g. k-means, single-link and CURE) perform much better than their original versions, especially when dealing with clustering tasks with clusters having arbitrary shapes and different scales.