PNMBG: Point Neighborhood Merging with Border Grids
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...
| Permalink: | http://skupni.nsk.hr/Record/nsk.NSK01000737101/Details |
|---|---|
| Matična publikacija: |
Journal of information and organizational sciences 33 (2009), 2 ; str. 297-305 |
| Glavni autor: | Wan, Renxia (-) |
| Ostali autori: | Chen, Jingchao (-), Wang, Lixin, Su, Xiaoke |
| Vrsta građe: | Članak |
| Jezik: | eng |
| Predmet: | |
| Online pristup: |
Journal of Information and Organizational Sciences |
| LEADER | 02048caa a2200313 ir4500 | ||
|---|---|---|---|
| 001 | NSK01000737101 | ||
| 003 | HR-ZaNSK | ||
| 005 | 20131203120022.0 | ||
| 007 | ta | ||
| 008 | 100520s2009 ci ||| ||eng | ||
| 035 | |9 (HR-ZaNSK)739727 | ||
| 035 | |a (HR-ZaNSK)000737101 | ||
| 040 | |a HR-ZaNSK |b hrv |c HR-ZaNSK |e ppiak | ||
| 042 | |a croatica | ||
| 080 | |a 004 |2 MRF 1998. | ||
| 100 | 1 | |a Wan, Renxia | |
| 245 | 1 | 0 | |a PNMBG: Point Neighborhood Merging with Border Grids / |c Renxia Wan, Jingchao Chen, Lixin Wang, Xiaoke Su. |
| 246 | 3 | |a Point Neighborhood Merging with Border Grids | |
| 300 | |b Ilustr. | ||
| 504 | |a Bibliografija: 22 jed. | ||
| 520 | 8 | |a 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. | |
| 653 | 0 | |a Baza podatka |a Grupiranje podataka |a Grid |a PNMBG |a Algoritam | |
| 700 | 1 | |a Chen, Jingchao | |
| 700 | 1 | |a Wang, Lixin | |
| 700 | 1 | |a Su, Xiaoke | |
| 773 | 0 | |t Journal of information and organizational sciences |x 1846-3312 |g 33 (2009), 2 ; str. 297-305 |w nsk.(HR-ZaNSK)000623898 | |
| 981 | |b B04/09 | ||
| 998 | |a rado100520 |c vol9131203 | ||
| 856 | 4 | 2 | |u http://www.jios.foi.hr/index.php/jios |y Journal of Information and Organizational Sciences |