Abstract
Currently used data-mining algorithms treat data globally. Nevertheless, with such methods, potentially useful knowledge that relates to local phenomena may be undetected. In this paper, we introduce new patterns in a form of local frequent events and epochs, boundaries of which correspond to discovered changes in a data stream. A local frequent event is an event which occurs in some period of time frequently, but not necessarily in the whole data stream. Such an event will be called a frequent event in a data stream. An epoch is understood as a sufficiently large group of frequent events that occur in a similar part of the data stream. The epochs are defined in such a way that they do not overlap are separated by so called change periods. In the paper, we discuss some potential applications of the proposed knowledge. Preliminary experiments are described as well.
Research has been supported by grant No 3 T11C 002 29 received from Polish Ministry of Education and Science.
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Cabaj, K. (2007). Frequent Events and Epochs in Data Stream. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_50
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DOI: https://doi.org/10.1007/978-3-540-73451-2_50
Publisher Name: Springer, Berlin, Heidelberg
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