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Building a high accuracy classifier for classification is a problem in real applications. One high accuracy classifier used for this purpose is based on association rules. In the past, some researches showed that classification based on association rules (or class-association rules – CARs) has higher accuracy than that of other rule-based methods, such as(More)
The subsequence matching in large time-series databases has been being an interesting problem. Many methods have been proposed that cope with this problem in an adequate extend. One of good ideas is reducing properly the dimensionality of time-series data. In this paper, we propose a method to reduce the dimensionality of high-dimensional time-series data.(More)
Permutations of a set are used in many practical problems. There are some algorithms for generating permutations of a set, e.g. a reverse alphabetical order algorithm, an algorithm based on adjacent transpositions, an algorithm determining permutations from their reductions... But these algorithms are rather long and difficult to parallelize. In this paper(More)
In this paper we construct a new efficient simple algorithm to generate all permutations of a finite set. And then we extend the algorithm for generating all iterative permutations of a multi-set. Applying the parallelizing method based on output decomposition we parallelize this algorithm. Further, we use the parallel algorithm to solve an optimal problem(More)