Abstract: The fuzzy C-means clustering algorithm is sensitive to noisy data, and it fails to effectively utilize the supervised information contained in the small amount of labeled data in time series data. To address these problems, the paper proposes an improved robust semi-supervised fuzzy C-means clustering algorithm（SRFCM）. Firstly, a sample uncertainty analysis method based on Mahalanobis distance is proposed, and add it to the semi-supervised fuzzy C-means clustering（SFCM） modeling to eliminate the influence of noise points. On this basis, by improving the partial supervision mechanism of SFCM, the supervision ability of labeled data is increased. And in the clustering process, the time warped edit distance（TWED）, which can elastically measure the similarity of time series, is used instead of the traditional Euclidean distance. Through the experimental comparison of 7 groups of public time series datasets, the results show that the algorithm has excellent clustering effect.

Key words: time series, semi-supervised clustering, fuzzy C-means clustering, sample uncertainty, time warping edit distance

摘要： 针对时间序列模糊C均值聚类算法对噪声数据敏感，及其未能将数据中少量已标记数据所包含的监督信息进行有效利用的问题，提出了一种改进的鲁棒性半监督模糊C均值聚类算法。该算法中先使用马氏距离提出一种样本不确定性分析方法，并加入到半监督模糊C均值聚类建模中，以消除噪声点的影响。并改进半监督模糊C均值聚类的部分监督机制来加大已标记数据的监督能力。采用能够弹性度量时间序列相似性的时间扭曲编辑距离代替欧氏距离进行聚类。通过对7组公开的时间序列数据集进行实验对比，结果表明所提算法具有良好的聚类效果。

关键词: 时间序列, 半监督聚类, 模糊C均值聚类, 样本不确定性, 时间扭曲编辑距离