改进Parzen窗解决高维数据聚类的方法研究OACSCDCSTPCD
Study of clustering using improved Parzen window on high-dimension dataset
由于高维数据聚类的现实意义日益增强,而Parzen窗估计法仅对低维数据集聚类能获得良好的结果,随着维数增加,效率降低,因此对Parzen窗进行加权改进,通过多次仿真实验确定加权函数,将高维数据投射至低维空间,对其聚类,逐步投向高维空间,对结果矩阵进行优化处理,得到更为优良的聚类效果.
Due to the realistic meaning of clustering on high-dimension dataset, excellent result can be acquired when clustering dataset using traditional Parzen window on low-dimension. With the higher dimension, the efficiency decreases quickly. Through many simulate experiments, the weighting function is gained. The high-dimension dataset is shadowed on lower-dimensional space,and clustered,then shadowed back to higher-dimensional space. The result matrix is optimi…查看全部>>
柴毅;利节;唐婧
重庆大学,自动化学院,重庆,400030重庆大学,自动化学院,重庆,400030中国石油管道公司兰成渝输油分公司,成都,610036
信息技术与安全科学
高维数据Parzen窗聚类
high-dimensional datasetParzen windowclustering
《计算机工程与应用》 2011 (8)
135-137,3
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