Scaling up the DBSCAN algorithm for clustering large spatial databases based on sampling technique
| Autor: | Bian Fuling, He Yanxiang, Zhou Shui-geng, Guan Jihong |
|---|---|
| Rok vydání: | 2001 |
| Předmět: |
DBSCAN
Clustering high-dimensional data Multidisciplinary Database business.industry Computer science Sampling (statistics) Image processing OPTICS algorithm Pattern recognition computer.software_genre SUBCLU Pattern recognition (psychology) Data mining Artificial intelligence Cluster analysis business computer |
| Zdroj: | Wuhan University Journal of Natural Sciences. 6:467-473 |
| ISSN: | 1993-4998 1007-1202 |
| DOI: | 10.1007/bf03160286 |
| Popis: | Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling-based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering largescale spatial databases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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