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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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fuzzy c-means clustering algorithm with Unknown Number of clusters for Symbolic Interval Data

Fuzzy C-Means Clustering Algorithm with Unknown Number of Cl...

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Annual conference of the SIcE

作者： chuang, chen-chia Jeng, Jin-Tsong Li, chih-Wen Natl Ilan Univ Dept Elect Engn Ilan Taiwan Natl Farmosa Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9784907764302

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FcM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFcMwUNc clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FcM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFcMwUNc clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFcMwUNc clustering algorithm for the symbolic interval-values data.

关键词： Symbolic interval-values data competitive agglomeration clustering algorithm 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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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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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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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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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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Identification of coherent Generators Using fuzzy c-means clustering algorithm and construction of Dynamic Equivalent of Power System

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JOURNAL OF ELEcTRIcAL SYSTEMS 2010年第2期6卷 1-18页

作者： El-arini, Mahdi M. M. Fathy, Ahmed Zagazig Univ Fac Engn Elect Power & MC Dept Zagazig Egypt

This paper presents a new simple method to identify the coherent groups of generators based on two different techniques;the first one is based on six proposed coherency criterions introduced by using time response of the linearized power system model;the second one is based on the application of fuzzy c-means clustering algorithm (FcM). A new technique of constructing the dynamic equivalent of power system is also presented in this work. The results obtained by the first technique are compared to those obtained by FcM technique. The proposed method is applied on 14-Bus IEEE power system. The obtained results proved that the proposed method is highly effective in determining the coherent groups of generators and in constructing the dynamic equivalent of power system with high accuracy.

关键词： coherent groups of generator fuzzy c-means clustering algorithm Dynamic equivalents Network reduction Dynamic aggregation

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Application of principal component analysis and a fuzzy c-means clustering algorithm to wear debris morphology classification

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PROcEEDINGS OF THE INSTITUTION OF MEcHANIcAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY 2009年第J7期223卷 1059-1066页

作者： Wu, J. P. Yan, X. P. Jin, Z. M. Tipper, J. L. Yuan, c. Q. Zhou, X. c. Wuhan Univ Technol Reliabil Engn Inst Wuhan 430063 Hubei Peoples R China Univ Leeds Div Microbiol MBE Leeds W Yorkshire England

The application of principal component analysis (PcA) and fuzzy c-means clustering algorithm to the classification of ultrahigh molecular weight polyethylene (UHMWPE) wear debris from artificial joints has been described in this article. Wear particles were extracted and isolated from peri-prosthetic tissues collected during revision surgery, which was revised for loosening. The implant life of the hip prosthesis was 12 years. The particles were examined by scanning electron microscopy. Digitized particle images were analysed on a computer by specially developed software 'Image-Pro Plus'. The following 19 numerical descriptors were used to characterize the particles: particle area, length, width, perimeter, boundary fractal dimension, and shape parameters such as form factor, roundness, convexity, aspect ratio, and others. PcA algorithm was applied to reduce the amount of parameters to simplify the following calculation. Furthermore, main factors and important parameters such as mean diameter, equivalent circle diameter, and perimeter were found out by PcA. However, c-means clustering algorithm was applied to classify the UHMWPE wear debris into 4-7 clusters. The Xie-Beni index was introduced to determine the optimal number of clusters and illuminate the clustering validity The result of the calculation indicates that five clusters is the optimal clustering number. The feature of the debris in each cluster is also described in this article.

关键词： ultrahigh molecular weight polyethylene wear debris artificial joints principal component analysis fuzzy c-means clustering algorithm

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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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