Abstract
In this work we analyse the relationship between distance and generalisation operators for real numbers, nominal data and tuples in the context of hierarchical distance-based conceptual clustering (HDCC). HDCC is a general approach to conceptual clustering that extends the traditional algorithm for hierarchical clustering by producing conceptual generalisations of the discovered clusters. This makes it possible to combine the flexibility of changing distances for several clustering problems and the advantage of having concepts which are crucial for tasks as summarisation and descriptive data mining in general. In this work we propose a set of generalisation operators and distances for the data types mentioned before and we analyse the properties by them satisfied on the basis of three different levels of agreement between the clustering hierarchy obtained from the linkage distance and the hierarchy obtained by using generalisation operators.
This work has been partially supported by the EU (FEDER) and the Spanish MEC/MICINN under grant TIN2007-68093-C02 and the Spanish project "Agreement Technologies" (Consolider Ingenio CSD2007-00022). A. Funes was supported by a grant from the Alfa Lernet project and the UNSL.
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Funes, A., Ferri, C., Hernández-Orallo, J., Ramírez-Quintana, M.J. (2009). An Instantiation of Hierarchical Distance-Based Conceptual Clustering for Propositional Learning. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_63
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