提出了一种多维关联规则推荐系统，为客户提供更加准确的个性化推荐。

This paper presents a multidimensional association rules recommendation system which provides more exact personalization recommendation for customer.

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实验结果表明，新算法在对具有稀疏特性的多维关联规则的挖掘中体现了良好的性能。

The experimental results show that this new algorithm has proven its significant performance in the sparse multidimensional association rule mining.

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介绍了在数据立方体上对于不同可信度的数据进行分块的方法，阐述了基于数据立方体分块的多维关联规则挖掘的算法。

This paper introduces partition method in data cube with different confidence, expatiates on multidimensional association rule data mining algorithm based on data cube partition.

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