Abstract
With the increasing needs of location information, applications of geospatial information have gained a lot of attention in both research and commercial organizations. Extraction of geospatial knowledge from the information content has been thus becoming a important process. Among theses applications, a typical example is to discover relationships between various geospatial texts/data and specific locations. In this paper, we describe a location based text mining approach using Artificial Neural Networks (ANN) to classify texts into various categories based on their geospatial features, with the aims to discovering relationships between documents and zones. First, the collected documents were mapped to corresponding zones by the Adaptive Affinity Propagation (Adaptive AP) clustering techniques, then we performed framed maximize zones by means of Fuzzy ARTMAP (FAM) and Support Vector Machines (SVM) methods, allowing the results of relationships between documents and zones to be well presented. Eventually, we compared our experimental results with that of baseline models using Self-organizing maps (SOM) and Learning Vector Quantization (LVQ) methods. The preliminary results show that our platform framework has potential for geospatial knowledge discovery.
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Lee, CH., Yang, HC., Wang, SH. (2010). A Location Based Text Mining Method Using ANN for Geospatial KDD Process. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_37
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DOI: https://doi.org/10.1007/978-3-642-13318-3_37
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