Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Ordinal association rules for error identification in data sets
Proceedings of the tenth international conference on Information and knowledge management
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Discovery of Ordinal Association Rules
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Data Mining in Time Series Database
Data Mining in Time Series Database
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We introduce the boasting problem, wherein useful trends in historical ordinal data (rankings) are discovered. Claims of the form "our object was ranked r or better in x of the last t time units," are formalized, and maximal claims (boasts) of this form are defined under two natural partial orders. For the first partial order, we give an efficient and optimal algorithm for finding all such maximal claims. For the second, we apply a classical result from computational geometry to achieve an algorithm whose running time is significantly more efficient than that of a naïve one. Finally, we connect this boasting problem to a novel variation of the problem of finding optimized confidence association rules as originally posed by Fukuda, et al. [2], and give an efficient algorithm for solving a simplification of the new problem.