A Density-Based Method for the Identification of Non-Disjoint Clusters With Arbitrary and Non-Spherical Shapes





Overlapping Clustering, Non-disjoint clusters, Density-based Methods, Clusters with Non-Spherical Shapes


Overlapping clustering is an important challenge in unsupervised learning applications while it allows for each data object to belong to more than one group. Several clustering methods were proposed to deal with this requirement by using several usual clustering approaches. Although the ability of these methods to detect non-disjoint partitioning, they fail when data contain groups with arbitrary and non-spherical shapes. We propose in this work a new density based overlapping clustering method, referred to as OC-DD, which is able to detect overlapping clusters even having non-spherical and complex shapes. The proposed method is based on the density and distances to detect dense regions in data while allowing for some data objects to belong to more than one group.
Experiments performed on articial and real multi-labeled datasets have shown the effectiveness of the proposed method compared to the existing ones.


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How to Cite

Ben Ncir, C. E. (2021). A Density-Based Method for the Identification of Non-Disjoint Clusters With Arbitrary and Non-Spherical Shapes. Computer Science, 22(2). https://doi.org/10.7494/csci.2021.22.2.4002