Time complexity optimization of clustering for fMRI feature reconstruction

doi:10.3778/j.issn.1002-8331.2009.32.061

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (32): 193-196.DOI: 10.3778/j.issn.1002-8331.2009.32.061

• 工程与应用 • Previous Articles Next Articles

Time complexity optimization of clustering for fMRI feature reconstruction

LIN Wei^1，2，HE Hua-can²，AI Li-rong²，LI Mei²

1.College of Computer and Information Technology，Henan Normal University，Xinxiang，Henan 453007，China
2.School of Computer Science and Engineering，Northwestern Polytechnical University，Xi’an 710072，China

Received:2009-05-25 Revised:2009-06-25 Online:2009-11-11 Published:2009-11-11
Contact: LIN Wei

脑fMRI特征重建的分层快速聚类方法

林卫^1，2，何华灿²，艾丽蓉²，李梅²

1.河南师范大学计算机与信息技术学院，河南新乡 453007
2.西北工业大学计算机学院，西安 710072

通讯作者: 林卫

Abstract

Abstract: To solve the problem of reconstructing features of functional Region of Interest（fROI） from extracted voxels，Hierarchical Fast Clustering method（HFC） is proposed.Compared with the existing K-means clustering methods，this method saves more than 62% running time on condition of ensuring regression ability.

Key words: functional Magnetic Resonance Imaging（fMRI）, Region of Interest（ROI）, hierarchical clustering, K-means, feature reconstruction

摘要： 在对大脑fMRI感兴趣区域的分析中，利用特征选择所得到的筛选属性进行特征重建问题上，提出了分层快速聚类的分析方法，同已有K-均值聚类方法相比，在聚类有效性得到提高的前提下，总体降低了聚类的时间代价，并为后续的回归分析处理提供了精确保证。

关键词: 功能核磁共振成像, 感兴趣区域, 层次聚类, K-均值聚类, 特征重建

CLC Number:

TP181

LIN Wei^1，2，HE Hua-can²，AI Li-rong²，LI Mei². Time complexity optimization of clustering for fMRI feature reconstruction[J]. Computer Engineering and Applications, 2009, 45(32): 193-196.

林卫^1，2，何华灿²，艾丽蓉²，李梅². 脑fMRI特征重建的分层快速聚类方法[J]. 计算机工程与应用, 2009, 45(32): 193-196.

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Time complexity optimization of clustering for fMRI feature reconstruction

脑fMRI特征重建的分层快速聚类方法

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