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Chen L, Shen F, Tang Y, Wang X, Wang J. Algebraic Structure Based Clustering Method from Granular Computing Prospective. INT J UNCERTAIN FUZZ 2023. [DOI: 10.1142/s0218488523500083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Clustering, as one of the main tasks of machine learning, is also the core work of granular computing, namely granulation. Most of the recent granular computing based clustering algorithms only utilize the plain granule features without taking the granule structure into account, especially in information area with widespread application of algebraic structure. This paper aims at proposing an algebraic structure based clustering method from granular computing prospective. Specifically, the algebraic structure based granularity is firstly formulated based on the granule structure of an algebraic binary operator. An algebraic structure based clustering method is then proposed by incorporating congruence partitioning granules and homomorphically projecting granule structure. Finally, proof of the lattice at multiple hierarchical levels and comparative analysis of experimental cases validate the effectiveness of the proposed clustering method. The algebraic structure based clustering method can provide a general framework to perform granularity clustering using the algebraic granule structure information. It meanwhile advances the granular computing methods by combing the granular computing theory and the clustering theory.
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Affiliation(s)
- Linshu Chen
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
| | - Fuhui Shen
- Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Police Academy, Changsha 410138, China
| | - Yufei Tang
- Department of Computer and Electrical Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | - Xiaoliang Wang
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, China
| | - Jiangyang Wang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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Ahmed MM, Palaniswamy T. A novel TMGWO–SLBNC‐based multidimensional feature subset selection and classification framework for frequent diagnosis of breast lesion abnormalities. INT J INTELL SYST 2021. [DOI: 10.1002/int.22768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Marwa M. Ahmed
- Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia
| | - Thangam Palaniswamy
- Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia
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Huang K, Wen H, Liu H, Yang C, Gui W. A geometry constrained dictionary learning method for industrial process monitoring. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.08.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Mao Y, He Z. Dual-Y network: infrared-visible image patches matching via semi-supervised transfer learning. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01996-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wang X, Ding W, Liu H, Huang X. Shape recognition through multi-level fusion of features and classifiers. GRANULAR COMPUTING 2020. [DOI: 10.1007/s41066-019-00164-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Granular computing-based multi-level interactive attention networks for targeted sentiment analysis. GRANULAR COMPUTING 2020. [DOI: 10.1007/s41066-019-00163-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Abstract
AbstractThe use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs.
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van Zelst SJ, Mannhardt F, de Leoni M, Koschmider A. Event abstraction in process mining: literature review and taxonomy. GRANULAR COMPUTING 2020. [DOI: 10.1007/s41066-020-00226-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractThe execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., the focus of process mining, yields a detailed understanding of the process, e.g., we are able to discover the control flow of the process and detect compliance and performance issues. Most process mining techniques assume that the event data are of the same and/or appropriate level of granularity. However, in practice, the data are extracted from different systems, e.g., systems for customer relationship management, Enterprise Resource Planning, etc., record the events at different granularity levels. Hence, pre-processing techniques that allow us to abstract event data into the right level of granularity are vital for the successful application of process mining. In this paper, we present a literature study, in which we assess the state-of-the-art in the application of such event abstraction techniques in the field of process mining. The survey is accompanied by a taxonomy of the existing approaches, which we exploit to highlight interesting novel directions.
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Zhang C, Dai J. An incremental attribute reduction approach based on knowledge granularity for incomplete decision systems. GRANULAR COMPUTING 2019. [DOI: 10.1007/s41066-019-00173-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Li Q, Chen L, Li X, Xia S, Kang Y. An improved random forests approach for interactive lobar segmentation on emphysema detection. GRANULAR COMPUTING 2019. [DOI: 10.1007/s41066-019-00171-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Liu H, Chen SM, Cocea M. Subclass-based semi-random data partitioning for improving sample representativeness. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2018.11.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Zhao HH, Liu H. Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. GRANULAR COMPUTING 2019. [DOI: 10.1007/s41066-019-00158-6] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Nature-inspired framework of ensemble learning for collaborative classification in granular computing context. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0122-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Lagunes ML, Castillo O, Soria J, Garcia M, Valdez F. Optimization of granulation for fuzzy controllers of autonomous mobile robots using the Firefly Algorithm. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0121-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Liu H, Zhang L. Fuzzy rule-based systems for recognition-intensive classification in granular computing context. GRANULAR COMPUTING 2018. [DOI: 10.1007/s41066-018-0076-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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