Data Mining from Hybrid Data with Numerical Attributes and Graph Structures

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The purpose of this research project is to give theoretical foundations of data mining from hybrid data with numerical attributes and graph structures. Since HTML/XML files are considered to be tree structured data, methods for discovering characteristic patterns from tree structured data are useful. Based on Genetic Programming, we have implemented a discovery system for characteristic tree structured patterns from given positive and negative examples of tree structured data. Our tree structured patterns are tag tree patterns. Although variables in a tag tree pattern are structured variables which can be substituted by arbitrary trees, these variables are considered to be special edges in a tree. Then we have naturally applied Genetic Programming, which is a genetic method for tree structured objects, to implementing our discovery system. Inferring real-valued functions from numerical data obtained from experiments or observations is a basic learning method for data mining from numerical data. A recursive real is a real number which we can deal with on a computer. So we have investigated learnabilities of recursive real-valued functions such as prediction and finite prediction of recursive real-valued functions. Also we have given various learning algorithms for tree or graph structured data, including an algorithm for extracting structural features among words and polynomial time inductive inference algorithms from positive data for newly introduced classes of graph languages.