Towards Parameter-Free Clustering for Real-World Data

While many clustering algorithms have been published, existing algorithms are often afflicted by some problems in processing real-world data. We present an algorithm to deal with two of these problems in this paper. First, the majority of clustering algorithms depend on one or more parameters. Second, some algorithms are not suitable for clusters of Gaussian distribution, whereas clusters of many real datasets are of Gaussian distribution approximately. Our algorithm generates clusters sequentially, and each cluster is obtained by expanding an initial cluster. The initial cluster is extracted with the dominant set algorithm, and we study the correlation between the pairwise data similarity matrix and clustering result to determine the involved scaling parameter adaptively. In expanding the initial cluster, we improve the density peak algorithm so that the expansion will not cross the boundary between two clusters, and the involved density parameter has little influence on clustering results. In our algorithm, the cluster expansion enables our algorithm to work well with clusters of Gaussian distribution, and two involved parameters can be fixed or determined adaptively. Our algorithm goes a step forward in parameter-free clustering for real-world data, and it is shown to perform better than or comparably to some commonly used algorithms with parameters in experiments with synthetic datasets composed of Gaussian clusters and real datasets. (c) 2022 Elsevier Ltd. All rights reserved.