Recently, smart data has attracted great attention in the smart city community since it can provide valuable information to support intelligent services such as planning, monitoring, and decision making. However, it imposes a big challenge to explore smart data from big data gathered from smart city with various advanced fusion and analysis approaches. This paper proposes an incremental tensor-based fuzzy c-means approach (IT-FCM) for obtaining smart data from continuously generated big data. Specifically, a weighted version of the tensor-based fuzzy c-means approach (T-FCM) is firstly proposed to cluster the dataset that combines the previous cluster centroids and the new generated data. Aiming to improve the clustering efficiency, the old data objects are represented by the centroids to avoid repeat clustering. Furthermore, this paper presents an edge-cloud-aided clustering scheme to fuse big data from different sources and perspectives and further to implement co-clustering on the fused datasets for exploring smart data. Finally, the proposed IT-FCM approach is evaluated by comparing with T-FCM regarding clustering accuracy and efficiency on two different datasets in the experiments. The results state that IT-FCM outperforms T-FCM in clustering streaming big data in terms of accuracy and efficiency for obtaining smart data.

近年来，智能数据在智慧城市社区引起了极大的关注，因为它可以提供有价值的信息来支持规划、监控和决策等智能服务。然而，通过各种先进的融合和分析方法，从智慧城市收集的大数据中探索智能数据是一个巨大的挑战。本文提出了一种基于增量张量的模糊 c 均值方法 (IT-FCM)，用于从连续生成的大数据中获取智能数据。具体而言，首先提出了基于张量的模糊 c 均值方法 (T-FCM) 的加权版本，以对结合了先前聚类质心和新生成数据的数据集进行聚类。为了提高聚类效率，旧数据对象用质心表示，以避免重复聚类。此外，本文提出了一种边缘云辅助聚类方案，以融合来自不同来源和视角的大数据，并进一步在融合数据集上实施协同聚类以探索智能数据。最后，通过在实验中将两个不同数据集的聚类精度和效率与 T-FCM 进行比较来评估所提出的 IT-FCM 方法。结果表明，在获取智能数据的准确性和效率方面，IT-FCM 在聚类流式大数据方面优于 T-FCM。