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Interactive Subspace Clustering for Mining High-Dimensional Spatial Patterns
| Content Provider | Semantic Scholar |
|---|---|
| Author | Guo, Diansheng Peuquet, Donna Gahegan, Mark |
| Copyright Year | 2002 |
| Abstract | The unprecedented large size and high dimensionality of existing geographic datasets make complex patterns that potentially lurk in the data hard to find. Spatial data analysis capabilities currently available have not kept up with the need for deriving the full potential of these data. “Traditional spatial analytical techniques cannot easily discover new and unexpected patterns, trends and relationships that can be hidden deep within very large and diverse geographic datasets”(Miller and Han 2000). We are facing a datarich but knowledge-poor era. To bridge this gap, spatial data mining and knowledge discovery has been gaining momentum. Clustering is one of the most important tasks in data mining and knowledge discovery literature (Fayyad, Piatetsky-Shapiro et al. 1996). Spatial clustering has also long been used as an important process in geographic analysis (Openshaw, Charlton et al. 1987; Ester, Kriegel et al. 1996; Kang, Kim et al. 1997; Wang, Yang et al. 1997; Estivill-Castro and Lee 2000; Zhang and Murayama 2000; Harel and Koren 2001). |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.geovista.psu.edu/publications/10421.pdf |
| Alternate Webpage(s) | https://www.geovista.psu.edu/publications/10421.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
