Boundary Detection-Based Density Peaks Clustering

Clustering algorithms have a very wide range of applications on data analysis, such as machine learning, data mining. However, data sets often have problems with unbalanced and non-spherical distribution. Clustering by fast search and find of density peaks (DPC) is a density-based clustering algorithm which could identify clusters with non-spherical data. In real applications, this algorithm and its variants are not very effective for the division of unevenly distributed clusters, because they only use one indicator (the distance of neighbor points) to handle inner points and boundary points at the same time. To this end, we introduce a new indicator named asymmetry measure which enhances the ability of finding boundary points. Then we propose a boundary detection-based density peaks clustering (BDDPC) algorthm that combines the above two indicators, so that different clusters are separated from each other accurately and the purpose of improving the clustering effect is achieved. The BDDPC algorithm can not only cluster uniformly distributed data, but also cluster unevenly distributed data. In real life, the distribution of high-dimensional data sets are always unbalanced, so this algorithm has very important applications. Experimental results with synthetic and real-world data sets illustrate the effectiveness of our algorithm.