Abstract
A hybrid approach to pixel data mining for analysing map based thematic data for segregating, identifying and characterising New Zealand’s grape wine regions is elaborated. The approach consisting of self-organising map (SOM) based clustering and Top-Down Induction of Decision Tree (TDIDT) decision techniques provides a means to profiling New Zealand wine regions despite scale, resolution and extent related data analysis issues that pose constraints with traditional and even with contemporary methods, such as satellite imagery and landscape classification techniques. With the SOM-TDIDT approach viticulturist can gain further insights into existing wine regions already zoned based on traditional methods. It could also be used to evaluate the suitability of new terroirs for potential vineyards as the continued production of premium wines by the world famous wineries has already become a challenges due to recent climate change observed across a few wine regions in Australia and the Mediterranean.
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Shanmuganathan, S. (2014). A Hybrid Approach to Pixel Data Mining. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_54
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DOI: https://doi.org/10.1007/978-3-319-12637-1_54
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