Abstract
Deviation detection is an important problem of both data and text mining. In this paper we consider the detection of deviations in a set of texts represented as conceptual graphs. In contrast with statistical and distance-based approaches, the method we propose is based on the concept of generalization and regularity. Among its main characteristics are the detection of rare patterns (that attempt to give a generalized description of rare texts) and the ability to discover local deviations (deviations at different contexts and generalization levels). The method is illustrated with the analysis of a set of computer science papers.
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Montes-y-Gómez, M., Gelbukh, A., López-López, A. (2002). Detecting Deviations in Text Collections: An Approach Using Conceptual Graphs. In: Coello Coello, C.A., de Albornoz, A., Sucar, L.E., Battistutti, O.C. (eds) MICAI 2002: Advances in Artificial Intelligence. MICAI 2002. Lecture Notes in Computer Science(), vol 2313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46016-0_19
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DOI: https://doi.org/10.1007/3-540-46016-0_19
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