Mining fuzzy temporal patterns from process instances with weighted temporal graphs
AbstractThis paper presents an algorithm for mining fuzzy temporal patterns from a given process instance. The fuzzy representation of time intervals embedded between the activities is used for this purpose. Initially, the activities are portrayed with their temporal relationships through temporal graphs and then, the defined data structures are used to retrieve the data suitable for the proposed algorithm. Similar to the familiar k-itemsets and k-dim sequences, their counterparts are introduced in this work. The proposed process-instance level data structure generates an optimum number of temporal itemsets. The proposed algorithm differs from the other existing algorithms on this topic in the representation of the mined data and patterns. An example is provided to demonstrate the algorithm.
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Bibliographic InfoArticle provided by Inderscience Enterprises Ltd in its journal Int. J. of Data Analysis Techniques and Strategies.
Volume (Year): 1 (2008)
Issue (Month): 1 ()
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Web page: http://www.inderscience.com/browse/index.php?journalID=282
temporal data mining; fuzzy temporal patterns; weighted temporal graphs; process instance data; temporal itemsets; fuzzy representation; time intervals.;
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- Waleed Alsabhan, 2012. "Normalised support: a virtual angle of measurement of 'interestingness'," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 101-114.
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