Rule extraction from spatial data using local learning techniques
Woodford, Brendon J
We are now in the fourth decade where techniques such as fuzzy systems, statistics, neural networks and machine learning have all been developed and more recently applied for the purpose of spatial data mining. However these methods act as global learning models and subsequently may not be able to learn the subtle nature of these types of data sets. Local learning models such as the Support Vector Machine (SVM) and a more recent method such as that proposed by (Gilardi 2002) address the problem of global versus local learning but fail to offer many solutions as to what underlying patterns may exist within the data set in order to better understand the data set. In this paper we propose the Evolving Fuzzy Neural Network (EFuNN) as a model for a local learning mechanism for the purpose of predicting rainfall within a region of Switzerland and also use this model to generate rules and then to visualise these rules that may help to describe any patterns that may exist within the data set.
Conference: 17th Annual Colloquium of the Spatial Information Research Centre (SIRC 2005: A Spatio-temporal Workshop), Dunedin, New Zealand
Keywords: local learning; similarity metrics; machine learning; fuzzy systems; neurocomputing; Rule Extraction
Research Type: Conference or Workshop Item (Paper)