Summary
Mining patterns involving multiple values that are significantly relevant is a difficult but very important problem that crosses many disciplines. Multi-value association patterns, which generalize sequentially ordered patterns, are sets of associated values extracted from sampling outcomes of a random N-tuple. Because they are value patterns from multiple variables, they are more specifically defined than their corresponding variable patterns. They are also easier to interpret. Normally, they can be detected by statistical testing if the occurrence of a pattern event is significantly deviated from the expected according to a prior model or null hypothesis. When the null hypothesis presumes the values of a pattern to be independent, the alternative hypothesis asserts that the values as a whole are associated, allowing some values to be independent within the detected set. Recently, a special type of multi-value association pattern is proposed which we called nested high-order pattern (NHOP), which is a subtype of the high-order pattern (HOP). We discuss here these patterns together with a related one called consigned pattern (CP). Evaluations using relevant experiments of synthetic and biomolecular data are also included.
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Lui, T.W.H., Chiu, D.K.Y. (2009). Multi-value Association Patterns and Data Mining. In: Abraham, A., Hassanien, AE., de Leon F. de Carvalho, A.P., Snášel, V. (eds) Foundations of Computational, IntelligenceVolume 6. Studies in Computational Intelligence, vol 206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01091-0_8
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