Перегляд за автором "Sitnikov, D. E."

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  • Зображення мініатюри
    Публікація
    A method for knowledge representation and discovery based on composing and manipulating logical equations
    (Management Information Systems, 2002) Sitnikov, D. E.; D'Cruz, B.; Sitnikova, P. E.
    The logical correlation in knowledge bases and development of a method for knowledge representation and discovery of dependencies between selected data features is discussed. It is pointed out that a knowledge base containing links between information features is represented in the form of logical equations. It is stated that extensible mark-up language (XML) is suitable for the writing of knowledge base in a text file. A subset of XML is developed which allows the data storage in a text file and help in discovering hidden patterns in the data by manipulating XML nodes.
  • Зображення мініатюри
    Публікація
    Discovering salient data features by composing and manipulating logical equations
    (Management Information Systems, 2000) Sitnikov, D. E.; D'Cruz, B.; Sitnikova, P. E.
    The paper suggests a method of representing knowledge in the form of logical equations of a special type. We show that deductive inferences about the salient data features in a knowledge base can be represented by a set of logical equations. We describe how logical equations with finite predicates can be successfully used for the description of logical links between discrete features, and how this can be applied to pattern recognition and data mining. New knowledge about logical links between discrete features in the data can be obtained by eliminating variables from these equations with the help of the operation ∃, and we describe this process in detail. We consider the application of the operation ∃ to a logical equation as an analogue to a query in a database. The process results in a dependence between the features subsequent to application of the elimination procedure that is easier to interpret than the dependence represented in the original equations, and is obtained without the need for exhaustive searching. We recursively define a class of finite predicates that allow eliminating variables without increasing the size of the original equation, and show how this can be applied for knowledge discovery using logical data modelling.