Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Applying Data Mining Methods for Cellular Radio Network Planning
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Generalizing the Notion of Confidence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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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.