基于模糊Fisher准则的自适应降维模糊聚类算法
doi: 10.3724/SP.J.1146.2008.01550
Fuzzy Fisher Criterion Based Adaptive Dimension Reduction Fuzzy Clustering Algorithm
-
摘要: 该文指出曹苏群等人提出的基于模糊Fisher准则(FFC)的半模糊聚类算法(FFC-SFCA)中的一个推导错误，结合模糊紧性和分离性(FCS)聚类算法提出新的聚类算法：FFC-FCS。FFC-FCS充分利用FFC的特征提取和降维特性，交替运行原始数据空间中FFC和投影空间中的FCS，通过对降维数据的聚类实现对原始数据的聚类。FFC-FCS不仅对低维数据具有优异的分类性能而且对高维数据也表现出一定的分类优势。实验结果表明，FFC-FCS 的性能明显优于原有的FCS算法，FFC-SFCA算法以及经典的模糊C-均值(FCM )算法。Abstract: The derivation mistake in Caos Fuzzy Fisher Criterion (FFC) based Semi-Fuzzy Clustering Algorithm (FFC-SFCA) is pointed out. Combining Fuzzy Compactness and Separation (FCS) clustering algorithm, a new clustering algorithm, FFC-FCS, is proposed in this paper. FFC-FCS make full use of the feature extraction and dimension reduction characteristics of FFC, alternately running FFC in the original data space and FCS in the projection space, clustering the original data is accomplished by clustering the dimension reduction data. FFC-FCS not only shows excellent capability of classifying low dimensional data but also has a certain grade classification advantage with respect to high dimensional data. The experimental results show that FFC-FCS has super performance over original FCS, FFC-SFCA and classical Fuzzy C-Means(FCM).
-
计量
- 文章访问数: 3360
- HTML全文浏览量: 111
- PDF下载量: 883
- 被引次数: 0
下载:
