Abstract
Neighborhood is a central concept in data mining, and a bunch of definitions have been implemented, mainly rooted in geometrical or topological considerations. We propose here a statistical definition of neighborhood: our TourneBool randomization test processes an objects × attributes binary table in order to establish which inter-attribute relations are fortuitous, and which ones are meaningful, without requiring any pre-defined statistical model, while taking into account the empirical distributions. It ensues a robust and statistically validated graph. We present a full-scale experiment on one of the public access Reuters test corpus. We characterize the resulting word graph by a series of indicators, such as clustering coefficients, degree distribution and correlation, cluster modularity and size distribution. Another graph structure stems from this process: the one conveying the negative “counter-relations” between words, i.e. words which “steer clear” one from another. We characterize in the same way the counter-relation graph. At last we generate the couple of valid document graphs (i.e. links and anti-links) and evaluate them by taking into account the Reuters document categories.
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Lelu, A., Cadot, M. (2010). Statistically Valid Links and Anti-links BetweenWords and Between Documents: Applying TourneBool Randomization Test to a Reuters Collection. In: Guillet, F., Ritschard, G., Zighed, D.A., Briand, H. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00580-0_18
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DOI: https://doi.org/10.1007/978-3-642-00580-0_18
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