A dynamic density-based clustering method based on K-nearest neighbor

Many density-based clustering algorithms already proposed in the literature are capable of finding clusters with different shapes, sizes, and densities. Also, the noise points are detected well. However, many of these methods require input parameters that are static and must be defined by user. Since it is difficult for users to determine these parameters in large data sets, the proper determination of them has an effective role in the identification of a suitable clustering. Therefore, a challenge in this domain is how to reduce the number of input parameters, thereby reducing the errors caused by users' involvement. In order to handle this challenge, a dynamic density-based clustering (DDBC) method is proposed in this paper for clustering purposes, which needs the smallest number of parameters to be set by users since many of them are determined automatically. This method has the ability to distinguish close clusters with different densities in a dynamic manner. Additionally, it can detect outliers and noises before starting the clustering process without scanning these points. Several real and artificial data sets were used to examine the efficiency of the proposed method, and its outcomes were compared to those of other algorithms in this domain . The comparative results confirmed the acceptable performance of DDBC and its higher accuracy in clustering tasks.