| 基于NLPCA的聚类可视化方法 |
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| 引用本文： | 齐志,李季,赵晓丹.基于NLPCA的聚类可视化方法[J].吉林大学学报(信息科学版),2010,28(5):526-532. |
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| 作者姓名： | 齐志 李季 赵晓丹 |
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| 作者单位： | 长春职业技术学院,信息技术学院,长春,130033;吉林省经济管理干部学院,国际商务系,长春,130012 |
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| 摘 要： | 为了将高维输入空间的数据映射到低维空间，利用可视化技术探测数据的固有特性,提出了用非线性主成分分析（NLPCA:NonLinear Principal Component Analysis）和自组织映射网络相结合的方法对生物信息学中基因表达数据进行聚类可视化分析。实验结果表明,该方法有较高的分类正确率,用于基因表达数据的聚类分析是行之有效的。
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| 关 键 词： | 自组织特征映射神经网络 非线性主成分分析 聚类分析 可视化 |
Visualization Methods Based on NLPCA in Cluster |
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| QI Zhi,LI Ji,ZHAO Xiao-dan.Visualization Methods Based on NLPCA in Cluster[J].Journal of Jilin University:Information Sci Ed,2010,28(5):526-532. |
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| Authors: | QI Zhi LI Ji ZHAO Xiao-dan |
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| Institution: | 1.School of Information Technology,Changchun Vocational Institute of Technology, Changchun 130033, China;2.School of International Business,Jilin Province Economics and Management Cadres College,Changchun 130012,China |
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| Abstract: | To project high-dimensional data into low-dimensional space can be effectively utilized to visualize and explore properties of data. An approach of NLPCA (NonLinear Principal Component Analysis)and SOM(Self-Organizing Map) neural network is presented for clustering and visualization of gene expression data. The experiment results show that the performance of clustering gene expression data based on the SOM network is efficient. |
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| Keywords: | self-organizing map neural network nonlinear principal components analysis cluster analysis visualization |
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