Density core-based clustering algorithm with dynamic scanning radius

Clustering analysis has been widely used in many fields such as image segmentation, pattern recognition, data analysis, market researches and so on. However, the distribution patterns of clusters are natural and complex in many research areas. In other words, most real data sets are non-spherical or non-elliptical clusters. For example, face images and hand-writing digital images are distributed in manifolds and some biological data sets are distributed in hyper-rectangles. Therefore, it is a great challenge to detect clusters of arbitrary shapes in multi-density datasets. Most of previous clustering algorithms cannot be applied to complex patterns with large variations in density because they only find hyper-elliptical and hyper-spherical clusters through centroid-based clustering approaches or fixed global parameters. This paper presents DCNaN, a clustering algorithm based on the density core and the natural neighbor to recognize complex patterns with large variations in density. Density cores can roughly retain the shape of clusters and natural neighbors are introduced to find dynamic scanning radiuses rather than fixed global parameters. The results of our experiments show that compared to state-of-the-art clustering techniques, our algorithm achieves better clustering quality, accuracy and efficiency, especially in recognizing extremely complex patterns with large variations in density.