Information Visualization and Analysis for Knowledge Discovery: Using a Multi Self-organizing Mapping

We present a Kohonen-based self-organizing multi-map algorithm. It is called MultiSOM. Firstly, we consider the unsupervised competitive learning mode of mapping data. We examine the naming of classes and the division of the global map into logical areas. We expose the two extended capabilities of the MultiSOM: generalization and inter-map communication mechanisms. Next, we show how the MultiSOM is used for supporting the human analysts advantage in their knowledge discovery achievements. A case study emphasizes the inter-map communication process. The data are a set of patents on transgenic plants technology. We also show that the inter-map communication mechanism provides support for watching the plants on which patented genetic technology works. It is the source map. The other four related maps provide information about the plant parts that are concerned, the target pathology, the transgenic techniques used for making these plants resistant, and finally the firms involved in genetic engineering and patenting.