A Hybrid Artificial Neural Network (ANN) Approach to Spatial and Non-spatial Attribute Data Mining: A Case Study Experience

A hybrid artificial neural network (ANN) approach consisting of self-organising map (SOM) and machine learning techniques (top-down induction decision tree/TDIDT) to characterising land areas of interest is investigated using New Zealand’s grape wine regions as a case study. The SOM technique is used for clustering map image pixels meanwhile, the TDIDT is used for extracting knowledge from SOM cluster membership. The contemporary methods used for such integrated analysis of both spatial and non-spatial data incorporated into a geographical information system (GIS), are summarised. Recent approaches to characterise wine regions (viticulture zoning) are based on either a single or composite (multi-attribute) index, formulated generally using digital data (vector and raster) representing the variability in environmental and viticulture related factors(wine label ratings and price range) over different spatial and temporal scales. Meanwhile, the world’s current wine regions, already well-developed, were initially articulated based on either grapevine growth phenology (growing degree days/GDD, frost days, average/minimum temperature, berry ripening temperature range) or wine style/rating/taste attributes. For both approaches, comprehensive knowledge on local viticulture, land area, wine quality and taste attributes is a sine qua non. It makes the characterisation of newworld vineyards or new sites (potential vineyards), with insufficient knowledge on local viticulture/environment an impossible task. For such instances and in other similar not so well-known domains, the SOM-TDIDT approach provides a means to select ideal features (discerning attributes) for characterising, in this case, within New Zealand’s wine regions or even within vineyards also scientifically validating the currently used factors regardless of present day scale and resolution related issues.