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    An Efficient Federated Multiview Fuzzy C-Means Clustering Method
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Hu, Xingchen; Qin, Jindong; Shen, Yinghua; Pedrycz, Witold; Liu, Xinwang; Liu, Jiyuan
    Multiview clustering has been received considerable attention due to the widespread collection of multiview data from diverse domains and sources. However, storing multiview data across multiple devices in many real scenarios poses significant challenges for efficient data analysis. Federated learning framework enables collaborative machine learning on distributed devices while preserving privacy constraints. Even though there have been intensive algorithms on multiview fuzzy clustering, federated multiview fuzzy clustering has not been adequately investigated so far. In this study, we first develop the federated learning mode into multiview fuzzy clustering and realize the federated optimization procedure, called federated multiview fuzzy C-means clustering. Then, we design an original strategy of consensus prototype learning during federated multiview fuzzy clustering. It is termed as federated multiview fuzzy C-means consensus prototypes clustering (FedMVFPC). We also further develop the federated alternative optimization algorithm with proven convergence. This study also introduces the notion of clustering prototype communication within the federated learning framework, and integrates the clustering prototypes of different views into a unified optimization formulation. The experimental studies on various benchmark datasets demonstrate that the proposed FedMVFPC method improves the federated clustering performance and efficiency. It achieves comparable or better clustering performance against the existing state-of-the-art multiview clustering algorithms.
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    Measuring efficiency of the high-tech industry using uncertain multi-stage nonparametric technologies
    (Elsevier Ltd, 2023) Liu, Xinwang; Chen, Xiaoqing; Wu, Qun; Deveci, Muhammet; Delen, Dursun
    As an important role in China's economy, the high-tech industry should evaluate and analyze the innovation activities from a systematic perspective to obtain innovation efficiency, thus improving high-quality development. In fact, for the efficiency assessment system of the high-tech industry, the indicators information is imprecise due to the inherent randomness, measurement error, incomplete information on economic phenomena, etc. However, few studies to date have considered and described the imprecise information. Moreover, data indivisibilities and the economic scale of the high-tech industry cause nonconvex technologies. However, little research has been conducted using the nonconvex measure to estimate innovation efficiency. In this regard, this paper is the first to combine convex and nonconvex technologies with uncertainty theory in a multi-stage system to compare the efficiency of the high-tech industry. More specifically, this paper first divides the innovation activities of the high-tech industry into a technological development stage and an economic transformation stage from the perspective of the innovation value chain. Second, uncertainty theory is adopted to express imprecise information, and uncertain multi-stage nonparametric frontier techniques are constructed to measure the innovation efficiency of the high-tech industry. Third, the high-tech industrial efficiency evaluation based on two-stage nonparametric techniques is established. Empirical results indicate that efficiency in the technology development stage is higher, particularly under nonconvex. Furthermore, the inefficiency of the whole system is mainly due to the inefficiency in the economic transformation under nonconvex, while under convex, the primary reason becomes the joint inefficiency of the two stages. © 2022 Elsevier Ltd