Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
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Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Digital web library of a website with document clustering
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
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ICADL'06 Proceedings of the 9th international conference on Asian Digital Libraries: achievements, Challenges and Opportunities
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With the increasing amount of textual information available in electronic form, more powerful methods for exploring, searching, and organizing the available mass of information are needed to cope with this situation. This paper presents the SOMLIb digital library system, built on neural networks to provide text mining capabilities. At its foundation we use the Self-Organizing Map to provide content-based clustering of documents. By using an extended model, i.e. the Growing Hierarchical Self-Organizing Map, we can further detect subject hierarchies in a document collection, with the neural network adapting its size and structure automatically during its unsupervised training process to reflect the topical hierarchy. By mining the weight vector structure of the trained maps our system is able to select keywords describing the various topical clusters. Text mining has to incorporate more than the mere analysis of content. Structural and genre information are key in organizing and locating information. Using color-coding techniques we can integrate a structural analysis of documents based on Self-Organizing Maps into the subject-based clustering relying on metaphor graphics for intuitive visualization. We demonstrate the capabilities of the SOMLib system using collections of articles from various newspapers and magazines.