PNMBG: Point Neighborhood Merging with Border Grids

Renxia Wan, Jingchao Chen, Lixin Wang, Xiaoke Su


The special clustering algorithm is attractive for the task of grouping arbitrary shaped database into several proper classes. Up to now, a wide variety of clustering algorithms designed for this task have been proposed, the majority of these algorithms is density-based. But the effectivity and efficiency still is the great challenges for these algorithms as far as the clustering quality of such task is concerned. In this paper, we propose an arbitrary shaped clustering method with border grids (PNMBG), PNMBG is a crisp partition method. It groups objects to point neighborhoods firstly, and then iteratively merges these point neighborhoods into clusters via grids, only bordering grids are considered during the merging stage. Experiments show that PNMBG has a good efficiency especially on the database with high dimension. In general, PNMBG outperforms DBSCAN in the term of efficiency and has an almost same effectivity with the later.


Clustering; Grid clique; Point neighborhood; Border grids; Merging

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Journal of Information and Organizational Sciences (Online)
ISSN 1846-9418 (online)
ISSN 1846-3312 (print)