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On the complex causal relationship of barriers to sustainable urban water management: a fuzzy multi-criteria analysis

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URBAN WATER JOURNAL 2021年第1期18卷 12-24页

作者： Selerio, Egberto F. Arcadio, Redjie D. Medio, Gerwine J. Natad, Jon Redgie P. Pedregosa, Girly A. Univ San Jose Recoletos Dept Ind Engn Cebu Philippines Univ San Carlos Dept Ind Engn Cebu Philippines Cebu Technol Univ Dept Ind Technol Cebu Philippines

A few studies on urban water management have engaged in identifying Sustainable Urban Water Management (SUWM) barriers. Most of these studies proposed many strategies to address them. However, for a developing country like the Philippines, it is impractical to employ all of them at once, especially when resources are scarce. Furthermore, due to the system complexity, conventional analysis, which approaches SUWM barriers in isolation, may not be sufficient. With an end goal of developing leverage strategies based on the causal relationship of SUWM barriers, integration of the fuzzy Decision-Making and Trial Evaluation Laboratory (FDEMATEL), and fuzzy c-means algorithm (FcA) is proposed. The approach was employed to identify the critical SUWM barriers among a set of SUWM barriers based on their causal relationship. critical SUWM barriers were identified. Insights and proposals for addressing the critical SUWM barriers were proposed herewith to guide urban water managers.

关键词： Urban water water management sustainability Multi-criteria Decision Making fuzzy DEMATEL fuzzy c-means algorithm

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cLUSTER VALIDITY BASED ON THE HARD TENDENcY OF THE fuzzy cLASSIFIcATION

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PATTERN REcOGNITION LETTERS 1990年第1期11卷 7-12页

作者： RIVER, FF ZAPATA, EL cARAZO, JM UNIV AUTONOMA MADRID CTR BIOL MOLECMADRID 34SPAIN

We present two new fuzzy cluster validity functionals (minimum and mean hard tendencies), based on the analysis of the hard tendency of the fuzzy classification generated by the fuzzy c -means algorithm. We have used the bootstrap technique, to avoid the possible influence of local minimums, obtained by the fuzzy c -means algorithm.

关键词： boottrap technique cluster validity fuzzy c-means algorithm

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Traffic Operation Data Analysis and Information Processing Based on Data Mining

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AUTOMATIc cONTROL AND cOMPUTER ScIENcES 2019年第3期53卷 244-252页

作者： Jiang, Zhihuang Guangdong Pei Zheng Coll Huadu Dist 510830 Guangdong Peoples R China

With the acceleration of urbanization, urban traffic problems are becoming more and more prominent. In the face of massive traffic data, it is difficult to predict traffic condition with effective data analysis methods. In order to deal with traffic data better, this study applied data mining in traffic data analysis and processing, constructed a Hadoop based data analysis system to collect and preprocess data, and analyzed traffic data using parallel distributed calculation based on MapReduce. The improved fuzzy c-means (FcM) algorithm and the random forest algorithm were used. The simulation results showed that the error rate of the improved FcM algorithm is 10% and the accuracy rate of the random forest algorithm is 92.3%, indicating the system had high reliability. Then an experiment was carried out on the main traffic roads in Huadu district of Guangzhou, china. It was found that the method was efficient and accurate and had a good application prospect.

关键词： data mining traffic operation Hadoop information processing fuzzy c-means algorithm random forest algorithm

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fuzzy K-NEAREST NEIGHBOR cLASSIFIERS FOR VENTRIcULAR ARRHYTHMIA DETEcTION

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INTERNATIONAL JOURNAL OF BIO-MEDIcAL cOMPUTING 1991年第2期27卷 77-93页

作者： cABELLO, D BARRO, S SALcEDA, JM RUIZ, R MIRA, J UNIV MURCIA ETSII CARTAGENADEPT INGN ELECTROMECANMURCIASPAIN UNIV NACL EDUC DISTANCIA FAC CIENCIASDEPT INFORMATMADRIDSPAIN

We report a study of the efficiency of 4 classifiers (the K-nearest-neighbor and single-nearest-prototype algorithms, each as parametrized by both fuzzy c-means and fuzzy covariance clustering) in the detection of ventricular arrhythmias in EcG traces characterized by 4 features derived from 7 spectral parameters. Principal components analysis was used in conjunction with a cardiologist's deterministic classification of 90 EcG traces to fix the number of trace classes to 5 (ventricular fibrillation/flutter, sinus rhythm, ventricular rhythms with aberrant complexes and 2 classes of artefact). Forty of the 90 traces were then defined as a test set;5 different learning sets (numbering 25, 30, 35, 40 and 45 traces) were randomly selected from the remaining 50 traces;each learning set was used to parametrize both the classification algorithms using both fuzzy clustering algorithms and the parametrized classification algorithms were then applied to the test set. Optimal K for K-nearest-neighbor algorithms and optimal cluster volumes for fuzzy covariance algorithms were sought by trial error to minimize classification differences with respect to the cardiologist's classification. fuzzy covariance clustering afforded significantly better perception of cluster structure than the fuzzy c-means algorithm, and the classifiers performed correspondingly with an overall empirical error ratio of just 0.10 for the K-nearest-neighbor algorithm parametrized by fuzzy covariance.

关键词： fuzzy K-NN cLASSIFIERS EcG PROcESSING VENTRIcULAR ARRHYTHMIA DETEcTION fuzzy c-means algorithm fuzzy cOVARIANcE algorithm

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Segmentation techniques for tissue differentiation in MRI of Ophthalmology using fuzzy clustering algorithms

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MAGNETIc RESONANcE IMAGING 2002年第2期20卷 173-179页

作者： Yang, MS Hu, YJ Lin, KcR Lin, ccL Chung Yuan Christian Univ Dept Math Chungli 32023 Taiwan Nanya Inst Technol Dept Management Informat Syst Chungli Taiwan Natl Yang Ming Univ Med Sch Ophthalmol Taipei 112 Taiwan

This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FcM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFcM), was used for MRI segmentation in Ophthalmology. These unsupervised segmentation algorithms can help Ophthalmologists to reduce the medical imaging noise effects originating from low resolution sensors and/or the structures that move during the data acquisition. They may be particularly helpful in the clinical oncological field as an aid to the diagnosis of Retinoblastoma, an inborn oncological disease in which symptoms usually show in early childhood. For the purpose of early treatment with radiotherapy and surgery, the newly proposed AFcM is preferred to provide more information for medical images used by Ophthalmologists. comparisons between FcM and AFcM segmentations are made. Both fuzzy clustering segmentation techniques provide useful information and good results. However, the AFcM method has better detection of abnormal tissues than FcM according to a window selection. Overall, the newly proposed AFcM segmentation technique is recommended in MRI segmentation. (c) 2002 Elsevier Science Inc. All rights reserved.

关键词： image segmentation magnetic resonance image (MRI) fuzzy c-means algorithm alternative fuzzy c-means algorithm

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DYNAMIc HAND GESTURE SEGMENTATION METHOD BASED ON UNEQUAL-PROBABILITIES BAcKGROUND DIFFERENcE AND IMPROVED FcM algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE cOMPUTING INFORMATION AND cONTROL 2015年第5期11卷 1823-1834页

作者： Zhang, Qiuyu Lu, Junchi Wei, Huiyi Zhang, Moyi Duan, Hongxiang Lanzhou Univ Technol Sch Comp & Commun 287 Langongping Rd Lanzhou 730050 Gansu Peoples R China

To extract complete hand gesture region under complex dynamic background and to effectively solve problems of skin-color interference and varying illumination, we present a novel dynamic hand gesture segmentation method which combines unequal probabilities and improved fuzzy c-means (FcM) algorithm. Firstly, this method utilizes unequal-probabilities to build background model of complex dynamic background and detects motion region of hand gesture with background difference. Secondly, FcM algorithm is improved to accelerate the rate of convergence, cluster hand gesture image and distinguish skin-color region and non-skin-color region. Finally, we process motion region and skin-color region with logic operation and morphological operation, and complete dynamic gesture segmentation under complex background. The experimental results illustrate that the proposed method has high accuracy and is robust to skin-color interference and varying illumination.

关键词： Human-computer interaction Dynamic hand gesture segmentation Unequal-probabilities background difference fuzzy c-means algorithm Morphological processing

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comparative study on textual data set using fuzzy clustering algorithms

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KYBERNETES 2016年第8期45卷 1232-1242页

作者： Sadika, Rjiba Soltani, Moez Benammou, Saloua Univ Econ & Management Sousse Tunisia High Sch Engineers Tunis ENSIT Dept Elect Engn Tunis Tunisia Fac Droit & Sci Econ & Polit Sousse Sousse Tunisia

Purpose - The purpose of this paper is to apply the Takagi-Sugeno (T-S) fuzzy model techniques in order to treat and classify textual data sets with and without noise. A comparative study is done in order to select the most accurate T-S algorithm in the textual data sets. Design/methodology/approach - From a survey about what has been termed the "Tunisian Revolution," the authors collect a textual data set from a questionnaire targeted at students. Five clustering algorithms are mainly applied: the Gath-Geva (G-G) algorithm, the modified G-G algorithm, the fuzzy c-means algorithm and the kernel fuzzy c-means algorithm. The authors examine the performances of the four clustering algorithms and select the most reliable one to cluster textual data. Findings - The proposed methodology was to cluster textual data based on the T-S fuzzy model. On one hand, the results obtained using the T-S models are in the form of numerical relationships between selected keywords and the rest of words constituting a text. consequently, it allows the authors to interpret these results not only qualitatively but also quantitatively. On the other hand, the proposed method is applied for clustering text taking into account the noise. Originality/value - The originality comes from the fact that the authors validate some economical results based on textual data, even if they have not been written by experts in the linguistic fields. In addition, the results obtained in this study are easy and simple to interpret by the analysts.

关键词： Analysis data fuzzy c-means algorithm Gath-Geva algorithm Kernel fuzzy c-means algorithm Modified Gath-Geva algorithm Textual data

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A RELAXATION APPROAcH TO THE fuzzy cLUSTERING PROBLEM

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fuzzy SETS AND SYSTEMS 1994年第2期61卷 177-188页

作者： KAMEL, MS SELIM, SZ KING FAHD UNIV PETR & MINERALS DEPT SYST ENGNDHAHRANSAUDI ARABIA

In this paper a new algorithm for fuzzy clustering is presented. The proposed algorithm utilizes the idea of relaxation. convergence of the proposed algorithm is proved and limits on the relaxation parameter are derived. Stopping criteria and resulting convergence behaviour of the algorithms are discussed. The performance of the new algorithm is compared to the fuzzy c-means algorithm by testing both on three published data sets. Theoretical and empirical results reported in this paper show that the new algorithm is more efficient and leads to significant computational savings.

关键词： fuzzy c-means algorithm fuzzy cLUSTERING RELAXATION TEcHNIQUES cLUSTER ANALYSIS PATTERN REcOGNITION

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coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones

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INTERNATIONAL JOURNAL OF GEOGRAPHIcAL INFORMATION ScIENcE 2020年第10期34卷 1930-1952页

作者： Liang, Xun Liu, Xiaoping chen, Guangliang Leng, Jiye Wen, Youyue chen, Guangzhao Sun Yat Sen Univ Sch Geog & Planning Guangdong Key Lab Urbanizat & Geosimulat Guangzhou Peoples R China China Univ Geosci Sch Geog & Informat Engn Wuhan Hubei Peoples R China Guangzhou Landtu Geog Informat Technol Co Ltd Guangzhou Peoples R China Univ Toronto Dept Geog & Planning Toronto ON Canada South China Inst Environm Sci MEE Guangzhou Peoples R China

Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (cA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified cA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, china from 2013 to 2019. The proposed model is applied to future simulation in 2019-2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs.

关键词： Economic development zone urban emergence simulation fuzzy c-means algorithm cellular automata

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Performance analysis for clustering algorithms

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INTERNATIONAL JOURNAL OF cOMPUTING ScIENcE AND MATHEMATIcS 2016年第5期7卷 485-493页

作者： Xue, Yu Zhao, Binping Ma, Tinghuai Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing 210044 Jiangsu Peoples R China Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Jiangsu Peoples R China

There are lots of algorithms for optimal clustering. The main part of clustering algorithms includes the K-means, fuzzy c-means (FcM) and evolution algorithm. The main purpose of this paper was to research the performance and characteristics of these three types of algorithms. One criteria (clustering validity index), namely TRW, was used in the optimisation of classification and eight real-world datasets (glass, wine, ionosphere, biodegradation, connectionist bench, hill-valley, musk, madelon datasets), whose dimension became higher, were applied. We made a performance analysis and concluded that it was easy of the K-means and FcM to fall into a local minimum, and the hybrid algorithm was found more reliable and more efficient, especially on difficult tasks with high dimension.

关键词： optimal clustering K-means fuzzy c-means algorithm hybrid differential evolution algorithm performance analysis high dimension

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