Yazar "Tang, Yiming" seçeneğine göre listele

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    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.
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    A Fuzzy Clustering Validity Index Induced by Triple Center Relation
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Tang, Yiming; Huang, Jiajia; Pedrycz, Witold; Li, Bing; Ren, Fuji
    The existing clustering validity indexes (CVIs) show some difficulties to produce the correct cluster number when some cluster centers are close to each other, and the separation processing mechanism appears simple. The results are imperfect in case of noisy data sets. For this reason, in this study, we come up with a novel CVI for fuzzy clustering, referred to as the triple center relation (TCR) index. The originality of this index is twofold. On the one hand, a new fuzzy cardinality is built on the strength of the maximum membership degree, and a novel compactness formula is constructed by combining it with the within-class weighted squared error sum. On the other hand, starting from the minimum distance between different cluster centers, the mean distance as well as the sample variance of cluster centers in the statistical sense are further integrated. These three factors are combined by means of product to form a triple characterization of the relationship between cluster centers, and hence a 3-D expression pattern of separability is formed. Subsequently, the TCR index is put forward by combining the compactness formula with the separability expression pattern. By virtue of the degenerate structure of hard clustering, we show an important property of the TCR index. Finally, based on the fuzzy C-means (FCMs) clustering algorithm, experimental studies were conducted on 36 data sets (incorporating artificial and UCI data sets, images, the Olivetti face database). For comparative purposes, 10 CVIs were also considered. It has been found that the proposed TCR index performs best in finding the correct cluster number, and has excellent stability.
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    Knowledge-Induced Multiple Kernel Fuzzy Clustering
    (Ieee Computer Soc, 2023) Tang, Yiming; Pan, Zhifu; Hu, Xianghui; Pedrycz, Witold; Chen, Renhao
    The introduction of domain knowledge opens new horizons to fuzzy clustering. Then knowledge-driven and data-driven fuzzy clustering methods come into being. To address the challenges of inadequate extraction mechanism and imperfect fusion mode in such class of methods, we propose the Knowledge-induced Multiple Kernel Fuzzy Clustering (KMKFC) algorithm. First, to extract knowledge points better, the Relative Density-based Knowledge Extraction (RDKE) method is proposed to extract high-density knowledge points close to cluster centers of real data structure, and provide initialized cluster centers. Moreover, the multiple kernel mechanism is introduced to improve the adaptability of clustering algorithm and map data to high-dimensional space, so as to better discover the differences between the data and obtain superior clustering results. Second, knowledge points generated by RDKE are integrated into KMKFC through a knowledge-influence matrix to guide the iterative process of KMKFC. Third, we also provide a strategy of automatically obtaining knowledge points, and thus propose the RDKE with Automatic knowledge acquisition (RDKE-A) method and the corresponding KMKFC-A algorithm. Then we prove the convergence of KMKFC and KMKFC-A. Finally, experimental studies demonstrate that the KMKFC and KMKFC-A algorithms perform better than thirteen comparison algorithms with regard to four evaluation indexes and the convergence speed.
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    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.
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    Universal Quintuple Implicational Algorithm: A Unified Granular Computing Framework
    (Ieee-Inst Electrical Electronics Engineers Inc, 2024) Tang, Yiming; Chen, Jingjing; Pedrycz, Witold; Ren, Fuji; Zhang, Li
    In the field of fuzzy inference, the universal triple I algorithm integrated the CRI (Compositional Rule of Inference) algorithm with the triple I algorithm. Later the triple I algorithm was generalized to the QIP (quintuple implication principle) algorithm. Whether the QIP algorithm and the CRI algorithm can be unified has become an interesting question. Therefore, in this study, a fuzzy inference scheme referred to as the universal quintuple implicational (UQI) algorithm is proposed. First, we establish a unified granular computing framework with the UQI algorithm, which is a generalization of the QIP algorithm, the CRI algorithm as well as the universal triple I algorithm. The optimal UQI solutions derived from the fundamental principle of determining inference results are obtained for the FMP (fuzzy modus ponens) problem, in which some specific solutions are also given. Second, the reversible property of the UQI algorithm is verified for FMP, while aiming at the metric derived from the biresiduum operation, the robustness of the UQI algorithm is validated. Third, under the environment of multiple rules, two general cases of FITA (First-Inference-Then-Aggregate) and FATI (First-Aggregate-Then-Inference) are constructed for the UQI algorithm. The corresponding equivalence relation between continuity and interpolation is analyzed. Fourth, the fuzzy system is established based on the UQI algorithm, the singleton fuzzier as well as the centroid defuzzier. Its response ability is analyzed and it is shown that such fuzzy system is a universal approximator. Lastly, we compare the results of the UQI algorithm with the QIP algorithm by five examples for FMP. It is found that the UQI algorithm is able to acquire more and better forms of the fuzzy inference in contrast with the QIP algorithm.