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This work investigates the problem of privacy-preserving mining of association rules. Specifically, a fake transaction randomization method is presented to protect the privacy of data. This method ensures the privacy of data by mixing real transactions with fake transactions. An algorithm for reconstructing frequent itemsets from the mixture of both fake(More)
Keywords: Stock forecasting Trading points prediction Back propagation network (BPN) Case based reasoning (CBR) Dynamic time window a b s t r a c t Stock forecasting involves complex interactions between market-influencing factors and unknown random processes. In this study, an integrated system, CBDWNN by combining dynamic time windows, case based(More)
Many real world data are associated with intervals of time or distance. Mining <i>frequent intervals</i> from such data allows the users to group transactions with similar behavior together. Previous work only focuses on the problem of mining frequent intervals in a discrete domain. This paper first proposes the notion of <i>maximal frequent intervals</i>,(More)