Clustering by Comparitive Density Peaks using FuzzyNeighborhood

As an important unsupervised learning method in machine learning, clustering has a wide range of applications in image processing and biological gene classification. "Clustering by fast search and find of density peaks" (DPC) proposes to classify data by looking for density peaks, which does not require an iterative process or too many parameter inputs. However, the DPC algorithm performs poorly on the spherical dataset, and it is easy to ignore the potential cluster center, and needs to manually participate in the cluster center selection. In view of the above problems, this paper uses the fuzzy neighborhood relationship to calculate the data density, and uses the comparative distance instead of the relative distance in the DPC algorithm. Through the experiment of machine learning data set, we compared our algorithm with DBSCN, OPTICS and DPC in accuracy and ARI. The experimental results show that the proposed algorithm is feasible and effective.