PNMBG: Point Neighborhood Merging with Border Grids

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 effi...

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Bibliographic Details
Published inJournal of Information and Organizational Sciences Vol. 33; no. 2; p. 297
Main Authors Wan, Renxia, Chen, Jingchao, Wang, Lixin, Su, Xiaoke
Format Paper
LanguageEnglish
Published Fakultet organizacije i informatike Sveučilišta u Zagrebu 15.12.2009
Subjects
Border grids
Clustering
Grid clique
Merging
Point neighborhood
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Summary: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.
Bibliography:45065
ISSN:1846-3312
1846-9418