TOD: Temporal outlier detection by using quasi-functional temporal dependencies
Data & Knowledge Engineering
Detection of fuzzy association rules by fuzzy transforms
Advances in Fuzzy Systems - Special issue on Fuzzy Functions, Relations, and Fuzzy Transforms (2012)
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The article suggests a partitioning algorithm for quantitative attributes to support the discovery of frequent fuzzy patterns among transactions containing such attributes. More precisely, we present a heuristic, multivariate, top-down partitioning algorithm that divides attribute ranges into such intervals that the discovered frequent sets are also dense, and thus probably more interesting to the user. Our approach is fuzzy, so that the derived intervals have fuzzy bounds, and thereby also the derived frequent sets are fuzzy. The crisp (nonfuzzy) case is obtained as a special case. We evaluate the goodness of the partitioning method by measuring the average and absolute information amounts of the obtained fuzzy frequent sets. For the mining task, any fuzzy frequent item set mining method can be used. Experiments show that the algorithm is able to do multidimensional partitioning in a balanced way, and the “interestingness” of the obtained frequent sets is quite high, especially for correlated attributes. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1111–1126, 2004.