Hierarchical Clustering based on Kernel Density Estimation
Deyi Li · Acta Simulata Systematica Sinica · 2004
Clustering is a promising application area for many fields including statistics, pattern recognition, data mining, etc. Among many clustering techniques, density-based method is one of the effective and efficient clustering methods that can discover clusters with arbitrary shape and is insensitive to noise data. According to the DENCLUE algorithm, we present a new hierarchical clustering approach based on kernel density estimation. In our approach, the window-width s is optimized to obtain good density estimation, then the density attractors are chosen to generate the center-defined data partition, and finally the center-defined clusters are iteratively merged into a hierarchy of clusters according to the saddles of density function. Theory analysis and experimental results show that this approach not only keeps the good features of DENCLUE, but also requires no input parameters and can discover clusters with arbitrary shapes and densities at different levels.