Abstract
Data mining is sometimes treating data consisting of items representing measurements of a single property taken in different time points. In this case data can be understood as a time series of one feature. It is no exception when the clue for evaluation of such data is related to their development trends as observed in several successive time points.
From the qualitative point of view one can distinguish 3 basic types of behavior between two neighboring time points: the value of the feature is stable (remains the same), it grows or it falls. This paper is concerned with identification of typical qualitative development patterns as they appear in the windows of given length in the considered time-stamped data and their utilization for specification of interesting subgroups.
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Nováková, L., Štěpánková, O. (2009). Visualization of Trends Using RadViz. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds) Foundations of Intelligent Systems. ISMIS 2009. Lecture Notes in Computer Science(), vol 5722. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04125-9_9
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DOI: https://doi.org/10.1007/978-3-642-04125-9_9
Publisher Name: Springer, Berlin, Heidelberg
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