Discovery Knowledge and Data Mining from Structured Data

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The purpose of this research project is to give theoretical foundations of data mining systems from graph-structured data or tree-structured data. Recently, Web documents such as HTML files and XIML files have increased rapidly. Such Web documents have no rigid structure and are called semistructured data. In general, such semistructured Web documents are represented by rooted trees. We have proposed methods for discovering frequent tree structured patterns in semistructured Web documents by using a tag tree pattern as a hypothesis. A tag tree pattern is an edge labeled tree which has ordered or unordered children and structured variables. An edge label is a tag or a keyword in such Web documents, and a variable can be substituted by an arbitrary tree. So a tag tree pattern is suited for representing tree structured patterns in such Web documents. Information Extraction from semistructured data becomes more and more important. In order to extract meaningful or interesting contents from semistructured data, we need to extract common structured patterns from semistructured data. We have presented a method for extracting characteristic tag tree patterns from irregular semistructured data by using an algorithm for finding a minimally generalized tag tree pattern explaining given data. Also we have given various learning algorithms of term trees, which are tree structured patterns with structured variables, from tree structured data, since such learning algorithms give theoretical foundations of data mining from semistructured data.