Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Connectionist learning procedures
Artificial Intelligence
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SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
A graphical, self-organizing approach to classifying electronic meeting output
Journal of the American Society for Information Science
Map displays for information retrieval
Journal of the American Society for Information Science
Self-organizing maps
Information Retrieval
Combining Symbolic and Numeric Techniques for DL Contents Classification and Analysis
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
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Self organization of a massive document collection
IEEE Transactions on Neural Networks
Clustering analysis for data samples with multiple labels
DBA'06 Proceedings of the 24th IASTED international conference on Database and applications
Clustering quality measures for data samples with multiple labels
DBA'06 Proceedings of the 24th IASTED international conference on Database and applications
Text mining techniques for patent analysis
Information Processing and Management: an International Journal
Visualization of patent analysis for emerging technology
Expert Systems with Applications: An International Journal
Development of a multilingual text mining approach for knowledge discovery in patents
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Knowledge extraction from unsupervised multi-topographic neural network models
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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The main area of this paper concerns the neural methods for mapping scientific and technical information (articles, patents) and for assisting a user in carrying out the complex process of analysing large quantities of such information.In the procedure of information analysis, like in the domain of patent analysis, the complexity of the studied topics and the accuracy of the question to be answered may often lead the analyst to partition his reasoning into viewpoints. Most of the classical information analysis tools can only manage an analysis of the studied domain in a global way. The information analysis tool that will be considered in our study is the MultiSOM tool whose core model represents a significant extension of the classical Kohonen SOM neural model. The MultiSOM neural-based tool introduces the concepts of viewpoints and dynamics into the information analysis with its multi-maps displays and its inter-map communication process. The dynamic information exchange between maps can be exploited by an analyst in order to perform cooperative deduction between several different analyzes that have been performed on the same data.The paper demonstrates the efficiency of a viewpoint-oriented-analysis as compared to a global analysis in the domain of patents. Both objective and subjective quality criteria are taken into account for quality evaluation.The experimental context of the paper is constituted by a patent database of 1000 patents related to oil engineering. The patents structure and the patents field semantics are firstly exploited in order to generate different viewpoints corresponding to different areas of interest for the analysts. In the experiment the selected viewpoints correspond to uses, advantages, patentees, and titles subfields of the patents. The indexing vocabulary of each viewpoint is automatically extracted of its related textual contents in the patents through a full text analysis. The resulting vocabulary is then used to rebuild patents descriptions regarding each viewpoint. These descriptions are finally classified through the unsupervised MultiSOM algorithm resulting in as much different maps as viewpoints. A fifth "global viewpoint" which represent the combination of all the specific ones is also considered in order to perform our comparison between a global classification mechanism and a pure viewpoint-oriented classification mechanism.