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Mining of Association Model Pair in Multidimensional Structured Database

机译:多维结构化数据库中关联模型对的挖掘

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In the paper we first propose a user preference model with the minimum support intelligent set method. Secondly, we propose a new data mining problem, the structure of the database to find frequent patterns associated pair. To effectively address these problems, we developed a series of cutting ability with a strong algorithm. The new algorithm is also discussed in the one-dimensional and multi-dimensional structure of the database found on the applicability of the model and to assess the efficiency of the new algorithm.
机译:在本文中,我们首先提出一种具有最小支持智能集方法的用户偏好模型。其次,我们提出了一个新的数据挖掘问题,即在数据库结构中查找频繁模式关联对。为了有效解决这些问题,我们开发了一系列具有强大算法的切削能力。还讨论了该新算法在数据库的一维和多维结构上的适用性,并评估了该新算法的效率。

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