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

ZHOU Liu-liu; YE Ning; XU Sheng; YAN Min-li; SUN Wei

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

更新时间：2026-09-04

- Multi-aspect Data Minizing on the Sparse Tensor Based on Dividing and Ruling
- Vol. 28, Issue 10, Pages: 204-208(2011)
- 作者机构：
  
  南京林业大学机构信息技术学院,江苏,南京,210037
- 作者简介：
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- Published：2011
- 稿件说明：
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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, et al. Multi-aspect Data Minizing on the Sparse Tensor Based on Dividing and Ruling[J]. 2011, 28(10): 204-208.
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. DOI：

ZHOU Liu-liu, YE Ning, XU Sheng, et al. Multi-aspect Data Minizing on the Sparse Tensor Based on Dividing and Ruling[J]. 2011, 28(10): 204-208. DOI：

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 METs 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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