FEM-DBSCAN: An Efficient Density-Based Clustering Approach

摘要

Due to the uncontrollable growth of data generation in various networks, rapid clustering of massive datasets is seriously demanded in order to reveal the hidden structure of data as well as discovering the relations among samples. Among the clustering approaches, density-based clustering methods showed an acceptable processing speed to encounter with big data. However, they have some fixed parameters, which are not certainly optimized for all parts of the feature space. Moreover, the complexity of these clustering methods is highly dependent on the number of samples. In this paper, we have deployed Fisher expectation maximization (FEM) to adaptively divide the feature space into some subspaces, where no cluster is shared between the adjacent subspaces. Afterward, we applied density-based spatial clustering of applications with noise (DBSCAN) to each partition yielding to decrease the computational complexity on each thread as well as better learning of its parameters on each subspace. The performance of the proposed method was assessed over three big-size and ten middle-size datasets. The achieved results implied the superiority of the proposed method to OPTICS, Den Clue and DBSCAN methods in terms of clustering accuracy (purity) and processing time.