周溜溜, 业宁, 徐昇, 严敏利, 孙伟. 基于分而治之策略的稀疏张量多层次数据挖掘[J]. 微电子学与计算机, 2011, 28(10): 204-208.
引用本文: 周溜溜, 业宁, 徐昇, 严敏利, 孙伟. 基于分而治之策略的稀疏张量多层次数据挖掘[J]. 微电子学与计算机, 2011, 28(10): 204-208.
ZHOU Liu-liu, YE Ning, XU Sheng, YAN Min-li, SUN Wei. Multi-aspect Data Minizing on the Sparse Tensor Based on Dividing and Ruling[J]. Microelectronics & Computer, 2011, 28(10): 204-208.
Citation: ZHOU Liu-liu, YE Ning, XU Sheng, YAN Min-li, SUN Wei. Multi-aspect Data Minizing on the Sparse Tensor Based on Dividing and Ruling[J]. Microelectronics & Computer, 2011, 28(10): 204-208.

基于分而治之策略的稀疏张量多层次数据挖掘

Multi-aspect Data Minizing on the Sparse Tensor Based on Dividing and Ruling

  • 摘要: 分析了MET算法的局限性以及out-of-core方法的特点, 融合了两种算法的思想提出一种基于分而治之策略的多层次数据挖掘算法 (DRMET), 避免了计算过程中可能造成的维数灾难问题, 克服了MET算法执行效率不高的缺陷, 同时继承了MET内存开销小的优点;实验结果表明:新算法在不增加存储空间的前提下大大约减了MET的时间开销, 其效率大约是MET的1.86~15.85倍.

     

    Abstract: Analyzed the limitations of MET algorithm and feature of out-of-core, mixed ideas of the two algorithms and then giving a method called DRMET which based on the strategy of dividing and ruling for multi-aspects data mining;avoid the problem of "dimension disaster" while overcomed the MET's limitation of low efficincy, and inherit its advantage on small memory consumed;experiment gives the result: new algorithm reduced time cost from MET substantially, its efficiency improved about 1.86~15.85 times compared to MET.

     

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