Research Paper on A DENSITY-BASED METHOD FOR THE IDENTIFICATION OF ARBITRARY AND NON-SPHERICAL SHAPES WITH DISJOINT AND NON DISJOINT CLUSTERS

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Title: A DENSITY-BASED METHOD FOR THE IDENTIFICATION OF ARBITRARY AND NON-SPHERICAL SHAPES WITH DISJOINT AND NON DISJOINT CLUSTERS

Abstract: The capacity of clustering techniques to construct both non-disjoint and disjoint data partitioning has grown to be a critical issue in unsupervised learning. Although this issue has been researched over the past few decades, several overlapping clustering methods have been proposed in the literature; however, the majority of these methods fail to look for clusters with arbitrary and non-spherical shapes. Additionally, the majority of these methods call for pre-configuring the number of clusters, which is a difficult task in practical clustering applications. Our new density-based overlapping clustering method, OC-DD, which can identify both disjoint and non-disjoint partitioning even when boundaries between clusters have complicated separations with arbitrary forms and shapes, is what we propose in this work as a solution to all of these problems. The suggested approach uses distance and density to identify connected groups and highly dense regions in data without the need to pre-configure the number of clusters. Studies on synthetic and real multi-labeled datasets have demonstrated the effectiveness of the suggested method when compared to the ones already in use.

Keywords: overlapping clustering, non-disjoint clusters, density-based methods, clusters with non-spherical shapes

Paper Quality: SCOPUS / Web of Science Level Research Paper

Paper type: Analysis Based Research Paper

Subject: Computer Science

Writer Experience: 20+ Years

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