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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)
Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. It divides its population of solutions into several sub-populations, and then searches for the optimal solution through two operations: assimilation and competition. The assimilation operation moves each non-best solution (called colony) in a sub-population toward the(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 multiobjective evolutionary algorithms are based Pareto domination, among them NSGA II and SPEA 2 are two very popular ones. MOEA/D is a very recent multiobjective evolutionary algorithm using decomposition. In this paper, we implement MOEA/D for multi-objective flowshop scheduling problems. We study the replacement strategy of neighboring solutions,(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)