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高维数据流聚类及其演化分析研究 被引量：9

Research on Clustering and Evolution Analysis of High Dimensional Data Stream
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摘要 基于数据流数据的聚类分析算法已成为研究的热点.提出一种基于子空间的高维数据流聚类及演化分析算法CAStream,该算法对数据空间进行网格化,采用近似的方法记录网格单元的统计信息,并将潜在密集网格单元快照以改进的金字塔时间结构进行存储,最后采用深度优先搜索方法进行聚类及其演化分析.CAStream能够有效处理高维数据流,并能发现任意形状分布的聚类.基于真实数据集与仿真数据集的实验表明,算法具有良好的适用性和有效性. Clustering analysis in data stream has become a hot research issue. In this paper, CAStream, a novel algorithm of clustering and evolution analysis over high dimensional data stream is presented, which is based on subspace. CAStream partitions the data space into grids, gets the grid summary statistics using approximate method, then stores snapshots of potential dense girds by improved pyramid time frame, and finally finds the clusters and analyzes the cluster evolution by the depth-first search algorithm. CAStream can deal with high dimensional data stream, and discover the clusters with arbitrary shape. The experimental results on real datasets and synthetic datasets demonstrate the promising availabilities of the approach.
作者 周晓云 孙志挥 张柏礼 杨宜东
机构地区 东南大学计算机科学与工程学院
出处 《计算机研究与发展》 EI CSCD 北大核心 2006年第11期2005-2011,共7页 Journal of Computer Research and Development
基金 国家自然科学基金项目(70371015) 教育部高等学校博士学科点科研基金项目(20040286009)
关键词 数据流 聚类分析 改进金字塔时间结构 演化分析 data stream clustering analysis improved pyramid time frame evolution analysis
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献10

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二级参考文献52

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共引文献161

同被引文献82

引证文献9

二级引证文献14

计算机研究与发展
2006年 第11期

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