Abstract
Many topics related to association mining have received attention in the research community, especially the ones focused on the discovery of interesting knowledge. A promising approach, related to this topic, is the application of clustering in the pre-processing step to aid the user to find the relevant associative patterns of the domain. In this paper, we propose nine metrics to support the evaluation of this kind of approach. The metrics are important since they provide criteria to: (a) analyze the methodologies, (b) identify their positive and negative aspects, (c) carry out comparisons among them and, therefore, (d) help the users to select the most suitable solution for their problems. Some experiments were done in order to present how the metrics can be used and their usefulness.
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de Carvalho, V.O., dos Santos, F.F., Rezende, S.O. (2013). Metrics to Support the Evaluation of Association Rule Clustering. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2013. Lecture Notes in Computer Science, vol 8057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40131-2_21
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DOI: https://doi.org/10.1007/978-3-642-40131-2_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40130-5
Online ISBN: 978-3-642-40131-2
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