Yazar "Gao, Jianwei" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    An overall framework of modeling, clustering, and evaluation for trapezoidal information granules
    (IEEE-INST electrical electronics engineers, 2024) Tang, Yiming; Gao, Jianwei; Pedrycz, Witold; Xi, Lei; Ren, Fuji
    In existing granular clustering algorithms, the design of coverage and specificity does not fully capture the inherent structural characteristics of granular data together with the optimization issue, and the current weight setting for the granular data is not sufficient. To address these problems, in this study, the trapezoidal information granule, which is rarely studied before, is concentrated, and we come up with a novel granular clustering algorithm called the weighted possibilistic fuzzy c-means algorithm for trapezoidal granularity (WPFCM-T). First, under the acknowledged principle of justifiable granularity, novel functions of coverage and specificity are designed for trapezoidal information granules, considering the internal characteristics of such granules. The idea of particle swarm optimization (PSO) is exploited to upgrade the established granular data, and then the trapezoidal information granule construction (TIGC) method is proposed to realize granular modeling. Second, an exponential weight is constructed with regard to coverage and specificity, while a novel distance via $\alpha$-cuts is given. The possibilistic fuzzy c-means structure is introduced into granular clustering, in which the new weight and distance are integrated, resulting in the proposed WPFCM-T algorithm. Third, the RC is studied to evaluate granular clustering, and hence an overall framework including granular modeling, clustering, and evaluation is constructed. Finally, through experiments completed on artificial datasets, UCI datasets, large datasets, high-dimensional datasets, and noisy datasets, WPFCM-T has superior granular data reconstruction ability by contrast with other granular clustering algorithms, indicating that the granular clustering performance of WPFCM-T is better than the others.
  • Küçük Resim Yok
    Öğe
    Clustering interval and triangular granular data: modeling, execution, and assessment
    (Institute of electrical and electronics engineers inc., 2024) Tang, Yiming; Wu, Wenbin; Pedrycz, Witold; Gao, Jianwei; Hu, Xianghui; Deng, Zhaohong; Chen, Rui
    In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the previous Euclidean distance.
  • Küçük Resim Yok
    Öğe
    Modeling and Clustering of Parabolic Granular Data
    (Institute of Electrical and Electronics Engineers Inc., 2024) Tang, Yiming; Gao, Jianwei; Pedrycz, Witold; Hu, Xianghui; Xi, Lei; Ren, Fuji; Hu, Min
    At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.