Abstract
Association rules constitute a widely accepted technique to identify frequent patterns inside huge volumes of data. Practitioners prefer the straightforward interpretability of rules, however, depending on the nature of the underlying data the number of induced rules can be intractable large. Even reasonably sized result sets may contain a large amount of rules that are uninteresting to the user because they are too general, are already known or do not match other user-related intuitive criteria. We allow the user to model his conception of interestingness by means of linguistic expressions on rule evaluation measures and compound propositions of higher order (i.e., temporal changes of rule properties). Multiple such linguistic concepts can be considered a set of fuzzy patterns (Fuzzy Sets and Systems 28(3):313–331, 1988) and allow for the partition of the initial rule set into fuzzy fragments that contain rules of similar membership to a user’s concept (Höppner et al., Fuzzy Clustering, Wiley, Chichester, 1999; Computational Statistics and Data Analysis 51(1):192–214, 2006; Advances in Fuzzy Clustering and Its Applications, chap. 1, pp. 3–30, Wiley, New York, 2007). With appropriate visualization methods that extent previous rule set visualizations (Foundations of Fuzzy Logic and Soft Computing, Lecture Notes in Computer Science, vol. 4529, pp. 295–303, Springer, Berlin, 2007) we allow the user to instantly assess the matching of his concepts against the rule set.
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References
Agrawal, R., Imielinski, T., & Swami, A. N. (1993). Mining association rules between sets of items in large databases. In P. Buneman & S. Jajodia (Eds.), Proc. ACM Sigmod Int. Conf. on Management of Data, Washington, D.C., May 26–28, 1993 (pp. 207–216). ACM Press.
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. I. (1996). Fast discovery of association rules. In Advances in knowledge discovery and data mining (pp. 307–328). Cambridge, MA: AAAI/MIT Press.
Borgelt, C. (2003). Efficient implementations of apriori and eclat. In Workshop frequent item set mining implementations (FIMI 2003, Melbourne, FL, USA). Aachen, Germany: CEUR Workshop Proceedings 90.
Borgelt, C. (2005). An implementation of the FP-growth algorithm. In Proc. workshop open software for data mining (OSDM 2005, Chicago, IL) (pp. 1–5). New York: ACM Press.
Steinbrecher, M., & Kruse, R. (2007). Visualization of possibilistic potentials. In Foundations of fuzzy logic and soft computing (Vol. 4529, pp. 295–303). Heidelberg: Springer.
Steinbrecher, M., Rügheimer, F., & Kruse, R. (2008, May). Application of graphical models in the automotive industry. In D. Prokhorov (Ed.), Computational intelligence in automotive applications (Vol. 132/2008, pp. 79–88). Heidelberg: Springer.
Yao, Y. Y., & Zhong, N. (1999). An analysis of quantitative measures associated with rules. In Methodologies for knowledge discovery and data mining. Berlin: Springer.
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Steinbrecher, M., Kruse, R. (2009). Clustering Association Rules with Fuzzy Concepts. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_18
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DOI: https://doi.org/10.1007/978-3-642-01044-6_18
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