Yazar "Qin, Jindong" seçeneğine göre listele

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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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    Optimizing-Information-Granule-Based Consensus Reaching Model in Large-Scale Group Decision Making
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Liang, Yingying; Pedrycz, Witold; Qin, Jindong
    In large-scale group decision making (LSGDM), the consensus result is expected to be realized explicitly through reconciling various preferences provided by decision makers based on their personalized viewpoints. An information-granule-consensus-based decision brings about high flexibility and promising aspects in group decision making. The consensus reaching proposals reported so far paid little attention to the merits of granular computing for managing LSGDM problems. This article concerns an extension of the well-known analytic hierarchy process to the LSGDM scenario using the optimizing-information-granule-based consensus reaching method. The consensus measurement is first quantified using coverage and specificity to derive the optimal cluster using the fuzzy C-means algorithm. Then, based on the optimization model of an information granule leading from numerical to interval representation, a novel construction model of information granule from interval representations to type-2 interval representation is developed, which yields the consistency of the obtained result instead of proceeding with an extra revision. To achieve the desired consensus, a preference modification algorithm is designed to detect the adjusted decision maker and further provide adjustment suggestions following the reference decision maker. Finally, a numeric study illustrates the effectiveness and flexibility of the proposed method.
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    Ranking products through online reviews: A novel data-driven method based on interval type-2 fuzzy sets and sentiment analysis
    (Taylor & Francis Ltd, 2024) Qin, Jindong; Zeng, Mingzhi; Wei, Xiao; Pedrycz, Witold
    As an essential information resource, online reviews play an important role in consumers' decision-making processes. To solve the product ranking problem through online reviews, two important issues are involved: sentiment analysis (SA) for online reviews and product ranking based on multi-criteria decision-making (MCDM) methods. However, merely a few studies have considered the impact of SA accuracy, which can significantly affect the final decision-making process. This paper proposes a novel data-driven method for ranking products through online reviews based on interval type-2 fuzzy sets (IT2FSs) and SA. In this method, after acquiring online reviews, the explicit and implicit attributes are extracted from the website itself and the latent Dirichlet allocation (LDA) model, respectively. Thereafter, a deep learning model is adopted to identify the five sentiment intensities of online reviews, based on which the SA results are represented as IT2FSs by considering the classification effect. After type-reduction for IT2FSs, the ranking order is obtained based on the exponential TODIM (ExpTODIM) method. Furthermore, a case study on ranking travel products from Trip.com Group through online reviews is provided to illustrate the effectiveness and applicability of the proposed method.