一种基于数据流的软子空间聚类算法  

Soft Subspace Clustering Algorithm for Streaming Data

作　　者：朱林[1,2] 雷景生[1] 毕忠勤[1] 杨杰[2] 

机构地区：[1]上海电力学院计算机科学与技术学院,上海200090 [2]上海交通大学图像处理与模式识别研究所,上海200240

出　　处：《软件学报》2013年第11期2610-2627,共18页Journal of Software

基　　金：国家自然科学基金(61273258;61272437;61073189);上海市自然科学基金(13ZR1417500);上海市教育委员会科研创新项目(14YZ131)

摘　　要：针对高维数据的聚类研究表明,样本在不同数据簇往往与某些特定的数据特征子集相对应.因此,子空间聚类技术越来越受到关注.然而,现有的软子空间聚类算法都是基于批处理技术的聚类算法,不能很好地应用于高维数据流或大规模数据的聚类研究中.为此,利用模糊可扩展聚类框架,与熵加权软子空间聚类算法相结合,提出了一种有效的熵加权流数据软子空间聚类算法——EWSSC(entropy-weighting streaming subspace clustering).该算法不仅保留了传统软子空间聚类算法的特性,而且利用了模糊可扩展聚类策略,将软子空间聚类算法应用于流数据的聚类分析中.实验结果表明,EWSSC算法对于高维数据流可以得到与批处理软子空间聚类方法近似一致的实验结果.A key challenge to most conventional clustering algorithms in handling many real life problems is that data points in different clusters are often correlated with different subsets of features. To address this problem, subspace clustering has attracted increasing attention in recent years. However, the existing subspace clustering methods cannot be effectively applied to large-scale high dimensional data and data streams. In this study, the scalable clustering technique to subspace clustering is extend to form soft subspace clustering for streaming data. An entropy-weighting streaming subspace clustering algorithm, EWSSC is proposed. This method leverages on the effectiveness of fuzzy scalable clustering method for streaming data by revealing the important local subspace characteristics of high dimensional data. Substantial experimental results on both artificial and real-world datasets demonstrate that EWSSC is generally effective in clustering high dimensional streaming data.

关 键 词：子空间聚类 数据流聚类 可扩展聚类 模糊聚类 文本聚类 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]