Abstract
The new form of quantitative and multi-dimensional association rules, unlike other approaches, does not require the discretization of real value attributes as a preprocessing step. Instead, associations are discovered with data-driven algorithms. Thus, such rules may be considered as a good tool to learn useful and precise knowledge from scientific, spatial or multimedia data, because data-driven algorithms work well with any sampling method. This paper presents the whole methodology of automatic discovery of new rules that includes theoretical background, algorithms, complexity analysis and postprocessing techniques. The methodology was designed for a specific telecom research problem, but it is expected to have a wide range of applications.
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