Introduction
One important type of complex knowledge can occur when mining data from multiple relations. In most domains, the objects of interest are not independent of each other, and are not of a single type. For example in World Wide Web
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Text has a list structure. We consider sequences of words.
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HTML has a tree structure (nested tags).
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Hyperlinks have a graph structure (linked pages).
In fact, most real domains have combinations of different types of internal and external structure nested at multiple levels of abstraction. We need data mining systems that can soundly mine the rich structure of relations among objects, such as interlinked Web pages, social networks, metabolic networks in the cell, etc. Yet another important problem is how to mine non-relational data. For example described by formulas of first-order logic.
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© 2009 Springer-Verlag Berlin Heidelberg
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Stepaniuk, J. (2009). Mining Knowledge from Complex Data. In: Rough – Granular Computing in Knowledge Discovery and Data Mining. Studies in Computational Intelligence, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70801-8_7
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DOI: https://doi.org/10.1007/978-3-540-70801-8_7
Publisher Name: Springer, Berlin, Heidelberg
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