Abstract
Many objective measures (OMs) were proposed since they are frequently used to discover interesting association rules. Therefore, an important challenge is to decide which OM to use. For that, one can: (a) reduce the number of OMs to be chosen; (b) aggregate OMs’ values in only one importance value as a mean of not selecting a suitable OM. The problem with (a) is that many OMs can remain. Regarding (b), the problem is that the obtained values cannot be well understandable. This work proposes a process to solve the problem related to the identification of a suitable OM to direct the users towards the interesting patterns. The goal is to find the same interesting patterns, as if the most suitable OM had been used, also trying to reduce the exploration space to minimize the user’s effort.
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Notes
- 1.
Developed by Christian Borgelt: http://www.borgelt.net/apriori.html.
- 2.
In fact, the cuts 0.25, 0.30, 0.35, 0.40, 0.45 and 0.50 were tested to see the impact of them in the results. As all of them behaved similarly, being the analysis described in Sect. 5 basically the same to all of them, we decided to present here only the results obtained in the first and in the last cut.
- 3.
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We wish to thank FAPESP and CAPES for the financial support.
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de Carvalho, V.O., de Padua, R., Rezende, S.O. (2016). Solving the Problem of Selecting Suitable Objective Measures by Clustering Association Rules Through the Measures Themselves. In: Freivalds, R., Engels, G., Catania, B. (eds) SOFSEM 2016: Theory and Practice of Computer Science. SOFSEM 2016. Lecture Notes in Computer Science(), vol 9587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49192-8_41
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