Improving research on Fuzzy C-Means algorithm based on reducing dimension by process encoding

doi:10.3778/j.issn.1002-8331.2010.30.043

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (30): 145-147.DOI: 10.3778/j.issn.1002-8331.2010.30.043

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Improving research on Fuzzy C-Means algorithm based on reducing dimension by process encoding

LIN Ming-wen，TAN Guo-zhen，DING Nan

Institute of Computer Application，Dalian University and Technology，Dalian，Liaoning 116024，China

Received:2009-07-07 Revised:2009-08-24 Online:2010-10-21 Published:2010-10-21
Contact: LIN Ming-wen

过程编码降维在FCM中的改进研究

林明文，谭国真，丁男

大连理工大学计算机应用研究所，辽宁大连 116024

通讯作者: 林明文

Abstract

Abstract: The characteristic of unsupervised clustering algorithm is used in reserving data feature effectively.A new method is proposed to reduce the dimension of data samples.Encoding some successive times of iterate results as feasible conditions to judge when dimensional cluster analysis start and when the algorithm stop.The new reducing dimension data can be used to go on clustering and extract data feature rapidly.The results of this experiment show that its effects of reducing dimension and the efficiency in executing speed of clustering algorithm.

Key words: clustering algorithm, reducing dimension, feature extraction

摘要： 利用无监督聚类算法可以有效地保留数据特征的特性，提出采用无监督聚类算法来对数据样本进行降维处理的方法，通过将连续多次迭代分类结果进行按类数编码，得到快速判定聚类分析降维开始的可行条件及聚类结束条件，并以降维数据为数据样本，继续进行聚类分析，快速完成数据特征提取。通过实验证明该方法在数据降维效果和聚类算法的执行速度上都有很大提高。

关键词: 聚类算法, 降维, 特征提取

CLC Number:

TP391.4

LIN Ming-wen，TAN Guo-zhen，DING Nan. Improving research on Fuzzy C-Means algorithm based on reducing dimension by process encoding[J]. Computer Engineering and Applications, 2010, 46(30): 145-147.

林明文，谭国真，丁男. 过程编码降维在FCM中的改进研究[J]. 计算机工程与应用, 2010, 46(30): 145-147.

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Improving research on Fuzzy C-Means algorithm based on reducing dimension by process encoding

过程编码降维在FCM中的改进研究

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