ClusterMPP: An unsupervised density-based clustering algorithm via Marked Point Process

Conventional clustering algorithms optimize a single criterion, which may not conform to diverse needs of multidimensional data science. This paper proposes a new clustering algorithm that solves multiple clustering issues, called clustering by Marked Point Process (ClusterMPP). It is a new, efficient, scalable and unsupervised density-based clustering algorithm. ClusterMPP simulates a proposed Marked Point Process (MPP) to find clusters of complex shapes present in the raw data space. The outputs of this new algorithm, in the first step, are the observations belonging to each cluster mode called "prototypes". The classification process is achieved, in the second step, using an improved KNN algorithm. We conduct intensive experiments to compare ClusterMPP with the most well-known algorithms. The results of ClusterMPP proved its efficiency on high complex and scalable datasets.