Spatial data mining: where to from here?
Woodford, Brendon J
The field of spatial data mining (Chawla, Shekhar,Wu & Ozesmi 2001), has been influenced by many other disciplines such as neural networks (Rumelhart, Hinton & Williams 1986), machine learning (Mitchell 1997), fuzzy systems (Zadeh 1965), and statistics (Sammon 1969). Recently other methods and techniques have been developed that offer some advantages over the conventional methods that have been applied in the past. For example the Support Vector Machine (SVM) (Cortes & Vapnik 1995) is one technique that can identify clusters where it may be difficult to easily separate different regions and new learning systems have now been developed that address the problem of local versus global learning models for spatial data analysis (Gilardi 2002). In this presentation we review the methods and techniques that have been previously employed for the purpose of spatial data mining and also introduce some new technologies that could be applied to this task.
Conference: 15th Annual Colloquium of the Spatial Information Research Centre (SIRC 2003: Land, Place and Space), Dunedin, New Zealand
Keywords: clustering; similarity metrics; machine learning; fuzzy systems
Research Type: Conference or Workshop Item (Oral presentation)
Only the abstract and references were published in the proceedings. There is no full text.