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Parallelism of spatial data mining based on autocorrelation decision tree 被引量：1

Parallelism of spatial data mining based on autocorrelation decision tree
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摘要 Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tree is presented. And the new method reduces CPU- and I/O-time and improves the query efficiency of spatial data. For dynamic load balancing, there are better control and optimization. Experimental performance comparison shows that the improved algorithm can obtain a optimal accelerator with the same quantities of processors. There are more completely accesses on nodes. And an individual implement of intelligent information retrieval for spatial data mining is presented. Define and theory of autocorrelation decision tree （ADT） is introduced. In spatial data mining, spatial parallel query are very expensive operations. A new parallel algorithm in terms of autocorrelation decision tree is presented. And the new method reduces CPU- and I/O-time and improves the query efficiency of spatial data. For dynamic load balancing, there are better control and optimization. Experimental performance comparison shows that the improved algorithm can obtain a optimal accelerator with the same quantities of processors. There are more completely accesses on nodes. And an individual implement of intelligent information retrieval for spatial data mining is presented.
作者 Zhang Shuyu Zhu Zhongying
机构地区 Dept. of Automation
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2005年第4期947-956,共10页 系统工程与电子技术（英文版）
关键词 spatial databases autocorrelation attribute decision tree parallelism. spatial databases, autocorrelation attribute, decision tree, parallelism.
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献13

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同被引文献32

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Journal of Systems Engineering and Electronics
2005年 第4期

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