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Behavioral correlation-Based Residential Space Modularization Using Design Structure Matrix and fuzzy c-means clustering algorithm

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BUILDINGS 2025年第4期15卷 647-647页

作者： Zeng, Fanbo Rao, Xiaojun Lei, Jianhua Huo, Xujie Shi, Yuan Ai, Deng Shenzhen Univ Sch Architecture & Urban Planning Shenzhen 518060 Peoples R China

This study introduces an automated method for constructing residential functional modules from the perspective of user behavior. By integrating the design structure matrix (DSM) and fuzzy c-means clustering algorithm (FcM), this approach systematically explores architectural functional modules. The DSM is employed to statistically analyze the correlations between residential behaviors. These correlations are then processed using FcM to generate various module segmentation schemes. The optimal scheme is selected based on modularity calculations. The results demonstrate improved modularity compared to traditional room-based designs, offering a greater variety of combinations and hierarchical organization. This methodology provides architects with a novel approach to address space integration challenges.

关键词： behavioral correlation modularization design structure matrix residential space fuzzy c-means clustering algorithm

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A Probabilistic Linguistic Three-Way Decision Method With Regret Theory via fuzzy c-means clustering algorithm

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IEEE TRANSAcTIONS ON fuzzy SYSTEMS 2023年第8期31卷 2821-2835页

作者： Zhu, Jinxing Ma, Xueling Martinez, Luis Zhan, Jianming Hubei Minzu Univ Sch Math & Stat Enshi 445000 Peoples R China Univ Jaen Dept Comp Sci Jaen 23071 Spain

Aiming at multiattribute decision-making (MADM) problems with probabilistic linguistic term sets (PLTSs), and considering the effective rationality of a decision-maker (DM) in complex decision environments, this article proposes a probabilistic linguistic three-way decision (TWD) method based on the regret theory (RT), namely, PL-TWDR. First, a probabilistic linguistic attribute weight determination method is developed that considers probabilistic linguistic information entropies and the weighted total deviation of all objects from the negative ideal solution (NIS). Then, a new group satisfaction index is designed to replace the utility function in RT, which overcomes the limitation of the RT calculation in PLTSs. Second, the fuzzy c-means (FcM) algorithm is extended to PLTSs for obtaining equivalent objects under different clusters and calculate conditional probabilities in corresponding TWD models, which makes up for the shortage of the PLTS evaluation matrix when dividing equivalence classes. Third, RT is introduced into PLTSs to rank objects according to utility perception values. At the same time, a new TWD model constructed by average utility perception values is used to realize object domains in probabilistic linguistic environments. Finally, the proposed method is applied to realistic cases, and the effectiveness and superiority of the PL-TWDR method are verified via comparative analysis and sensitivity analysis in terms of other nine popular decision-making methods.

关键词： fuzzy c-means clustering algorithm multi-attribute decision-making (MADM) probabilistic linguistic term set regret theory three-way decision

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A hybrid algorithm for classifying rock joints based on improved artificial bee colony and fuzzy c-means clustering algorithm

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GEOMEcHANIcS AND ENGINEERING 2022年第4期31卷 353-364页

作者： Ji, Duofa Lei, Weidong chen, Wenqin Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin 150090 Peoples R China Harbin Inst Technol Minist Ind & Informat Technol Key Lab Smart Prevent & Mitigat Civil Engn Disaste Harbin 150090 Peoples R China Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

This study presents a hybrid algorithm for classifying the rock joints, where the improved artificial bee colony (IABc) and the fuzzy c-means (FcM) clustering algorithms are incorporated to take advantage of the artificial bee colony (ABc) algorithm by tuning the FcM clustering algorithm to obtain the more reasonable and stable result. A coefficient is proposed to reduce the amount of blind random searches and speed up convergence, thus achieving the goals of optimizing and improving the ABc algorithm. The results from the IABc algorithm are used as initial parameters in FcM to avoid falling to the local optimum in the local search, thus obtaining stable classifying results. Two validity indices are adopted to verify the rationality and practicability of the IABc-FcM algorithm in classifying the rock joints, and the optimal amount of joint sets is obtained based on the two validity indices. Two illustrative examples, i.e., the simulated rock joints data and the field-survey rock joints data, are used in the verification to check the feasibility and practicability in rock engineering for the proposed algorithm. The results show that the IABc-FcM algorithm could be applicable in classifying the rock joint sets.

关键词： fuzzy c-means clustering algorithm improved artificial bee colony algorithm joint set rock mass

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computed tomography of ground glass nodule image based on fuzzy c-means clustering algorithm to predict invasion of pulmonary adenocarcinoma

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JOURNAL OF RADIATION RESEARcH AND APPLIED ScIENcES 2022年第1期15卷 152-158页

作者： Zhang, Min Zhu, Lin Sun, Yijun Niu, Duoshan Liu, Jianguang Peoples Hosp Xuancheng City Dept Radiol Xuancheng 242000 Peoples R China Peoples Hosp Xuancheng City Dept Cardio Thorac Surg Xuancheng 242000 Peoples R China Peoples Hosp Xuancheng City Dept Pathol Xuancheng 242000 Peoples R China

Early detection of lung cancer in computed tomography (cT) can significantly improve the survival rate of patients. A part of lung cancer early appeared in the form of ground glass nodule (GGN), and its early detection requires the help of a computer-aided algorithm. This study aimed to explore the cT features of GGN based on a fuzzy c-means (FcM) clustering algorithm in predicting the invasion of pulmonary adenocarcinoma. In this study, the lung parenchyma from 65 patients with GGN was segmented based improved FcM cluster algorithm. The region segmentation algorithm removed the useless area, and critical features of GGN extracted the suspicious area. After using the improved algorithm, the effect of region segmentation was superior;the simulation results show that the algorithm can remove blood vessels' "line" or branching structure well;the improved FcM had good real-time performance and robustness to noise. From the segmented GGN, the invasive adenocarcinoma (IA) had a larger size and higher cT attenuation than that of non-IA lesions. The results show that the accurate analysis of cT features of GGN based on the FcM clustering algorithm could be helpful in distinguish non-IA from IA, which is beneficial to determine strategies of follow-up or appropriate management for GGN.

关键词： Pulmonary adenocarcinoma Ground glass nodule fuzzy c-means clustering algorithm computed tomography

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EM-IFcM: fuzzy c-means clustering algorithm based on edge modification for imbalanced data

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INFORMATION ScIENcES 2024年 659卷

作者： Pu, Yue Yao, Wenbin Li, Xiaoyong Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Software & Multimedia Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China

The improved fuzzy c-means (IFcM) algorithm is an effective technique for handling the "uniform effect" in imbalanced data clustering;it adjusts the weight of each class based on the fuzzy size between clusters. However, the IFcM algorithm produces a "siphon effect" as the imbalance rate increases. It misclassifies the samples in small classes into large ones. Our analysis shows that this effect occurs because all samples have the same weight value of the same classes, the membership values are polarized, resulting in the model failing to converge to the correct interval. Thus, we propose an imbalanced fuzzy c-means clustering based on edge modification (EM-IFcM) algorithm to alleviate the "siphon effect" of the IFcM algorithm. It exhibits stronger inter-class separability by dynamically adjusting the weight of the samples to enhance the influence of edge samples on the model. In addition, we analyze the effectiveness and complexity of the algorithm and proved its convergence. Finally, we conduct extensive experiments on synthesis, machinelearning, and image-segmentation datasets and compare the results with those of six algorithms. The experimental results show that EM-IFcM has higher accuracy and exhibits an imbalance rate that is at least 1.94 times higher than that of the other algorithms.

关键词： Imbalanced data Uniform effect Siphon effect fuzzy c-means clustering algorithm

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Remote sensing image classification based on RBF neural network based on fuzzy c-means clustering algorithm

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2020年第4期38卷 3567-3574页

作者： Yu, changqing Wang, Liguang Zhao, Jiong Hao, Li Shen, Yafeng Xijing Univ Sch Informat Engn Xian Shaanxi Peoples R China Xian Univ Technol Engn Training Ctr Xian Shaanxi Peoples R China

With the development of modern remote sensing technology, remote sensing images have become one of the powerful tools for people to understand the Earth and its surroundings. However, there is currently no good classification algorithm that can accurately classify images. In order to accurately classify remote sensing images, this paper studies the content of the article by using fuzzy c-means clustering algorithm and radial basis neural network (RBF). The classification accuracy of SIRI-WHU dataset was analyzed by using the classification accuracy evaluation index such as overall accuracy and Kappa coefficient. The Kappa coefficient of vegetation classification in SIRI-WHU dataset was 0.9678, and the overall accuracy reached 97.18%. According to the classification problem of remote sensing image, according to the characteristics of remote sensing image, the improved model Alex Net-10-FcM is used to classify the remote sensing image dataset, and very high classification accuracy is obtained.

关键词： Remote sensing image classification fuzzy c-means clustering algorithm Kappa coefficient data set RBP neural network

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Adaptive Weighted fuzzy c-means clustering algorithm Based on Density Peaks

Adaptive Weighted Fuzzy C-Means Clustering Algorithm Based o...

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Artificial Intelligence and Electromechanical Automation (AIEA), International conference on

作者： Lina Ren Maoxuan Yao Guizhou Light Industry Technical College Guiyang China Guizhou Provincial Institute of Land Resources Survey and Planning Guiyang China

ISBN: (数字)9781728182889

ISBN: (纸本)9781728182896

The initial clustering center and membership matrix of the traditional FcM algorithm are randomly selected, so if there are outliers or uneven distribution of the data set, the FcM algorithm will fall into a local optimum, which will affect the clustering result. In view of the above problems, this paper proposes an adaptive weighted FcM algorithm based on density peaks. This algorithm improves the FcM algorithm by two points: first, the algorithm uses the density peak idea of the DPc algorithm to determine the initial clustering center, so as to improve the shortcomings of the FcM algorithm to randomly select the clustering center and reduce the number of iterations of the algorithm; Secondly, the algorithm uses an improved inverse cotangent function to construct the sample weight of each sample point for the class and uses it to improve the membership matrix of the FcM algorithm. In this way, the algorithm improves the shortcomings of FcM algorithm to randomly obtain membership matrix, and improves the accuracy of clustering. The experimental results show that the proposed algorithm has good clustering effect, smaller number of iterations and better time performance.

关键词： density peaks fuzzy c-means clustering algorithm Adaptive weighted Membership matrix algorithms cluster Analysis iterative methods Specimen weight Sample point clustering algorithms

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A cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm

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EURASIP JOURNAL ON WIRELESS cOMMUNIcATIONS AND NETWORKING 2019年第1期2019卷 1页

作者： Zhang, Shunchao Wang, Yonghua Li, Jiangfan Wan, Pin Zhang, Yongwei Li, Nan Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China South Cent Univ Nationalities Hubei Key Lab Intelligent Wireless Commun Wuhan 430074 Hubei Peoples R China

To improve spectrum sensing performance, a cooperative spectrum sensing method based on information geometry and fuzzy c-means clustering algorithm is proposed in this paper. In the process of signal feature extraction, a feature extraction method combining decomposition, recombination, and information geometry is proposed. First, to improve the spectrum sensing performance when the number of cooperative secondary users is small, the signals collected by the secondary users are split and reorganized, thereby logically increasing the number of cooperative secondary users. Then, in order to visually analyze the signal detection problem, the information geometry theory is used to map the split and recombine signals onto the manifold, thereby transforming the signal detection problem into a geometric problem. Further, use geometric tools to extract the corresponding statistical characteristics of the signal. Finally, according to the extracted features, the appropriate classifier is trained by the fuzzy c-means clustering algorithm and used for spectrum sensing, thus avoiding complex threshold derivation. In the simulation results and performance analysis section, the experimental results were further analyzed, and the results show that the proposed method can effectively improve the spectrum sensing performance.

关键词： cooperative spectrum sensing Information geometry Decomposition and recombination fuzzy c-means clustering algorithm

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Vertical co-current two-phase flow regime identification using fuzzy c-means clustering algorithm and ReliefF attribute weighting technique

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2016年 95卷 393-404页

作者： Pan, Liang-ming Zhang, Muhao Ju, Peng He, Hui Ishii, Mamoru Chongqing Univ Dept Nucl Engn Chongqing 400044 Peoples R China Purdue Univ Sch Nucl Engn 400 Cent Dr W Lafayette IN 47907 USA

In order to develop the flow regime identification map by objective method, air-water two-phase upward and downward flow experiments were conducted in a tubular test section with the inside diameter of 2.54 cm. This study focused on (1) a new objective flow regime identification method based on the fuzzy c-means (FcM) clustering algorithm and ReliefF attribute weighting algorithm, (2) the construction of the objective flow regime map on the basis of the identification results of the proposed objective method and (3) the discussion of entrance and LID effects for co-current upward and downward two-phase flow. The ReliefF-FcM method successfully identified flow regime and the flow regime maps obtained by this method showed the same trends as the maps of Mishima-Ishii and Usui for upward and downward flow, respectively. Furthermore, in comparing with the previous objective flow regime identification methods, the remarkable advantage of the method represented in the paper was discussed. By comparing the flow regime maps at different positions of test section, the entrance and LID effects on the flow regime transition were also considered. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： Two-phase flow Flow regime map fuzzy c-means clustering algorithm ReliefF cPDF Entrance effects

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Improving Wang-Mendel method performance in fuzzy rules generation using the fuzzy c-means clustering algorithm

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NEUROcOMPUTING 2015年第Part3期151卷 1293-1304页

作者： Gou, Jin Hou, Feng chen, Wenyu Wang, cheng Luo, Wei Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China

The generation of fuzzy rules from samples for fuzzy modeling and control is significant If samples contain noise and outliers, the Wang-Mendel (WM) method may lead to the extraction of invalid rules resulting in low confidence of the rules. The scale of the samples also affects the efficiency of the WM method. Interaction among input variables can help the WM method achieve high completeness and robustness. The fuzzy c-means clustering (FcM) algorithm can reduce the scale of samples and undo noisy data to some degree. This paper aims to develop an FcM-based improved WM method that adopts a modified FcM algorithm to preprocess the original samples and compute the interaction among the samples. Then, the optimized samples are used to generate fuzzy rules, thereby building a complete rule set through extrapolation. Experimental results from two nonlinear functions and short-term load forecasting case study show that the proposed method not only has high completeness and robustness, but also ensures better prediction accuracy of the fuzzy system. (c) 2014 Elsevier B.V. All rights reserved.

关键词： fuzzy system fuzzy c-means clustering algorithm Wang-Mendel method completeness Robustness

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