Titlebook: New Frontiers in Mining Complex Patterns; 4th International Wo Michelangelo Ceci,Corrado Loglisci,Zbigniew W. Ras Conference proceedings 20

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Conference proceedings 2016 richness of complex data and allow for efficient and effective identification of complex information units present in such data. The papers are organized in the following sections: data stream mining, classification, mining complex data, and sequences..

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Probabilistic Frequent Subtree Kernelsilistic nature, the embedding function corresponding to our graph kernel is not always correct. Our empirical results on artificial and real-world chemical datasets, however, demonstrate that the graph kernel we propose is much faster than other frequent pattern based graph kernels, with only marginal loss in predictive accuracy.

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0302-9743 present in such data. The papers are organized in the following sections: data stream mining, classification, mining complex data, and sequences..978-3-319-39314-8978-3-319-39315-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Generalizing Patterns for Cross-Domain Analogy abstracting from portions of knowledge that have no analogical relationships. The result is a multi-strategy approach in which the analogy brings to a generalization, that is, in turn, a novel description to reason over and over again. An example is provided to show the behavior and effect of the proposed generalization approach.

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