A Location Based Text Mining Approach for Geospatial Data Mining
Chung-Hong Lee, Hsin-Chang Yang, Shih‐Hao Wang · 2009
In this paper, we describe a location based text mining approach to classify texts into various categories based on their geospatial features, with the aims to discovering relationships between documents and zones. We first mapped documents into corresponding zones by adaptive affinity propagation (adaptive AP) clustering technique, and then framed maximize zones by means of simplified fuzzy ARTMAP (SFAM) and support vector machines (SVM) methods. Also, we compared our experimental results with the baseline approaches of self-organizing maps (SOM) and learning vector quantization (LVQ) methods. The preliminary results show that our platform framework has the potential for geospatial data mining.