计算机科学 ›› 2010, Vol. 37 ›› Issue (5): 155-156.
• 数据库与数据挖掘 • 上一篇 下一篇
贺玲,蔡益朝,杨征
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HE Ling,CAI Yi-chao,YANG Zheng
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摘要: 数据间的相似性度量是进一步分析数据集整体特性的一个重要基础。针对高维数据的相似性度量问题,提出了一种基于子空间的相似性度量方法。该方法先将高维空间进行基于网格的划分,然后在划分后的子空间内计算数据间的相似性。理论分析表明,在合理选定网格划分参数的前提下,该方法可有效减小“维度灾难”对高维数据相似性度量的影响。
关键词: 高维数据,维度灾难,网格划分,子空间,相似度量
Abstract: The similarity measurement among data is important for further analysis of the data set. Aiming at the similarity measurement of high dimensional data, the paper put forward a new method based on subspace. After dividing high dimensional space into grids, and computing the similarity among data in proper subspaces, the disturbance from the curse of dimensionality can be abated efficiently under the proper dividing parameters.
Key words: High dimensional data, Curse of dimcnsionality, Grid-based dividing, Subspace, Similarity mcasurcmcnt
贺玲,蔡益朝,杨征. 高维数据的相似性度量研究[J]. 计算机科学, 2010, 37(5): 155-156. https://doi.org/
HE Ling,CAI Yi-chao,YANG Zheng. Researches on Similarity Measurement of High Dimensional Data[J]. Computer Science, 2010, 37(5): 155-156. https://doi.org/
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