Maciej Piernik

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XML classification finds many applications, ranging from data integration to e-commerce. However, existing classification algorithms are designed for static XML collections, while modern information systems frequently deal with streaming data that needs to be processed on-line using limited resources. Furthermore, data stream classi-fiers have to be able to(More)
Classification of large, static collections of XML data has been intensively studied in the last several years. Recently however, the data processing paradigm is shifting from static to streaming data, where documents have to be processed online using limited memory and class definitions can change with time in an event called concept drift. As most(More)
Classification of large, static collections of XML data has been intensively studied in the last several years. Recently however, the data processing paradigm is shifting from static to streaming data, where documents have to be processed online using limited memory and class definitions can change with time in an event called concept drift. As most(More)
Now that the use of XML is prevalent, methods for mining semi-structured documents have become even more important. In particular, one of the areas that could greatly benefit from in-depth analysis of XML’s semi-structured nature is cluster analysis. Most of the XML clustering approaches developed so far employ pairwise similarity measures. In this paper,(More)
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