Titlebook: Knowledge, Data and Computer-Assisted Decisions; Martin Schader,Wolfgang Gaul Conference proceedings 1990 Springer-Verlag Berlin Heidelber

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Pyramidal Representation of Symbolic Objectsevelop methods that yield easily interpretable results. In this paper we show how we may enlarge the domain of the data at the input and obtain an “explained” output of a clustering method by adopting notions of Symbolic Data Analysis. We start by recalling the definitions and properties of symbolic

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Knowledge Representation and Symbolic Data Analysisadapted to represent knowledge which “unifies” instead of observations which characterize “individual things”: for instance, “the customers of my shop” instead of “a customer of my shop”, a “a kind of mushroom” instead of “the mushroom, that I have in my hand”. In Diday (1987) we have introduced sev

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Automated Acquisition of Production Rules by Empirical Supervised Learning Methods combines a data analysis technique for linearly classifying with a conceptual method for generating disjunctive cover for each class, taking advantage of the peculiarities of both the approaches. Initial empirical results are encouraging.

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Cluster and Classify: A Conceptual Approachfeasibility of establishing conceptual clustering schemes within the structure of an Object Oriented Programming System, namely KEE (Knowledge Engineering Environment). It is shown that both adequate data description and representation are crucial in order to successfully develop the method. The con

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Incremental Learning From Symbolic Objectsoisy and insufficiently representative, our approach is oriented towards successive approximations of a discriminant rule base; the aim is to predict conclusions according to further examples..A 2-step iterative process is presented:.First results on a. well-studied learning base are detailed.

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Some Algorithms for “Bond Energy” Data Analysis, Including Simulated Annealinggorithm” originally proposed by McCormick et al. (1972) for permuting rows and columns of data matrices into visually interpretable forms. To evaluate the performance of three variations of SA, they were compared to two deterministic, heuristic methods known to perform well for the particular type o

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