| 一种改进的基于密度和网格的高维聚类算法 |
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| 引用本文： | 朱倩,黄志军.一种改进的基于密度和网格的高维聚类算法[J].舰船电子工程,2005,25(5):55-56,59. |
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| 作者姓名： | 朱倩 黄志军 |
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| 作者单位： | 海军工程大学,武汉430033 |
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| 摘 要： | 提出了一种改进的基于密度和网格的高维聚类算法,并对算法有效性进行了验证.该算法通过减少样本点数量的方法达到减少稠密子空间数量.在发现高维稠密子空间时,对样本库进行精简.这些样本点的求得能有效减少求解最小聚类的时间复杂度.
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| 关 键 词： | 数据挖掘 聚类 网格 密度 高维数据 子空间 最小聚类 |
| 收稿时间： | 2005-04-26 |
| 修稿时间： | 2005-04-262005-05-08 |
Validity Validation of An Improved High - dimensional Cluster Analysis Algorithm Based on Grid and Intensity |
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| Zhu Qian ,Huang Zhijun.Validity Validation of An Improved High - dimensional Cluster Analysis Algorithm Based on Grid and Intensity[J].Ship Electronic Engineering,2005,25(5):55-56,59. |
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| Authors: | Zhu Qian Huang Zhijun |
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| Institution: | Navy University of Engineering,Wuhan 430033 |
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| Abstract: | This paper proposes an improved high-dimensional cluster analysis algorithm based on grid and intensity,then discusses it's validity validation.The amount of the density subspace can be deduced by cutting down that of sample data.The sample library is simplified as the high-dimensional subspaces are found. By working out such sample data the time complexity of figuring out min cluster is effectively reduced. |
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| Keywords: | data mining cluster grid density high - dimensional data subspace min cluster |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
