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Titlebook: Rough Sets, Fuzzy Sets, Data Mining and Granular Computing; 12th International C Hiroshi Sakai,Mihir Kumar Chakraborty,William Zhu Conferen

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0302-9743 puting, RSFDGrC 2009, held in Delhi, India in December 2009 in conjunction with the Third International Conference on Pattern Recognition and Machine Intelligence, PReMI 2009. RSFDGrC 2009 is the core component of a broader Rough Set Year in India initiative, RSIndia09. The 56 revised full papers pr
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Discovering Concurrent Process Models in Data: A Rough Set Approachd concurrency in the context of process mining in data. The main goal of this review is the general presentation of the research in this area. Discovering of concurrent systems models from experimental data tables is very interesting and useful not only with the respect to cognitive aspect but also
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Intelligent Science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain, research on the principles and models of natural intelligence at molecular, cellular, and behavior levels. Cognitive science studies human mental activities, such as perception, learning, memory, th
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Pattern Structures for Analyzing Complex Dataobject classes and dependencies between sets of attributes used for describing objects. In case of data more complex than binary to apply FCA techniques, one needs . (binarizing) data. Pattern structures propose a direct way of processing complex data such as strings, graphs, numerical intervals and
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Innovation Game as a Tool of Chance Discoveryis a tool for aiding innovative thoughts and communication, coming after our 10-year experiences in chance discovery where tools of data visualization have been applied in cases of decision making by business teams. This game enables us to run and accelerate the process of innovation, as well as to
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Rough Set Year in India 2009ding classification and prediction models. It complements a number of other soft computing paradigms. It may be combined with fuzzy logic and probabilistic data analysis. It has led towards enhancements of neural networks, genetic algorithms, clustering, support vector machines, regressionmodels, et
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