In multiple analysis tasks and personalized services, tremendous challenges in Cyber-Physical-Social Systems (CPSS) are clustering large-scale multi-source data and generating multiple distinct clusterings dependent on different applications. To address these challenges, this paper first presents two simple multiple clustering methods which can produce different clustering results according to arbitrarily selected combinations of features, one is similarity matrices-based multiple clusterings which computes the weighted average of similarity matrices for selected feature spaces, another is Euclidean distance-based multiple clusterings which fuses different feature spaces using selective weighted Euclidean distance. Furthermore, a tensor decomposition-based multiple clusterings is presented for efficiently clustering high-dimensional data, and a multi-relational attribute ranking method is further proposed to improve the clustering performance. This paper illustrates and evaluates the proposed methods on a design example and a real world data set. Experimental results show that the proposed methods can effectively cluster big data to provide enhanced knowledge extractions and services in CPSS.

中文翻译：

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用于网络物理社会应用的基于张量的多重聚类方法

在多分析任务和个性化服务中，网络物理社会系统（CPSS）面临的巨大挑战是对大规模多源数据进行聚类，并根据不同的应用生成多个不同的聚类。为了解决这些挑战，本文首先提出了两种简单的多重聚类方法，它们可以根据任意选择的特征组合产生不同的聚类结果，一种是基于相似度矩阵的多重聚类，它计算选定特征空间的相似度矩阵的加权平均值，另一种是基于相似度矩阵的多重聚类方法。是基于欧氏距离的多重聚类，它使用选择性加权欧氏距离融合不同的特征空间。此外，提出了一种基于张量分解的多重聚类，以有效地对高维数据进行聚类，并进一步提出了一种多关系属性排序方法来提高聚类性能。本文在一个设计实例和一个真实世界的数据集上说明和评估了所提出的方法。实验结果表明，所提出的方法可以有效地对大数据进行聚类，以在CPSS中提供增强的知识提取和服务。