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A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests

PATTERN RECOGNITION LETTERS 2005年第5期26卷 639-652页

作者： Wu, KL Yu, J Yang, MS Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan Beijing Jiaotong Univ Dept Comp Sci Beijing 100044 Peoples R China

Most clustering algorithms are based on a within-cluster scatter matrix with a compactness measure. In this paper we propose a novel fuzzy clustering algorithm, called the fuzzy compactness and separation (FCS), based on a fuzzy scatter matrix in which the FCS algorithm is derived using compactness measure minimization and separation measure maximization. The compactness is measured using a fuzzy within-cluster variation. The separation is measured using a fuzzy between-cluster variation. The proposed FCS objective function is a modification of the FS validity index proposed by Fukuyama and Sugeno and also a generalization of the fuzzy c-means (FCM). The FCS algorithm assigns a crisp boundary (cluster kernel) for each cluster such that hard memberships and fuzzy memberships can co-exist in the clustering results. Thus, FCS can be seen as a clustering algorithm with a novel sense between the hard e-means and fuzzy c-means. The FCS optimality tests and parameter selection are also investigated. Some numerical examples are demonstrated to show its robust properties and effectiveness. (c) 2004 Elsevier B.V. All rights reserved.

关键词： fuzzy clustering algorithm scatter matrix within-cluster variation between-cluster variation fuzzy compactness and separation

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A Robust Nonlocal fuzzy clustering algorithm With Between-Cluster Separation Measure for SAR Image Segmentation

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2014年第12期7卷 4929-4936页

作者： Ji, Jian Wang, Ke-Lu Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

fuzzy c-means (FCM) algorithm has been widely used in image segmentation, and there have been many improved algorithms proposed. But when dealing with synthetic aperture radar (SAR) images, they may not give satisfactory segmentation results because of speckle noise. In order to segment SAR image effectively, a robust fuzzy clustering algorithm is proposed, called nonlocal fuzzy clustering algorithm with between-cluster separation measure (NS_FCM). In NS_FCM, to reduce the effects of the noise, we incorporate the nonlocal spatial information obtained using an improved nonlocal mean method, which adopts adaptive binary weighted distance measure and adaptive filtering degree parameter. In addition, we introduce a fuzzy between-cluster variation term into the objective function. Based on this, while minimizing the objective function, we can maximize the within-cluster compactness measure and the between-cluster separation measure of the partition simultaneously. Besides, by regulating the parameter of the fuzzy between-cluster variation term, we can adjust the distance between the clustering centers flexibly. This makes NS_FCM more effective to the images, which have some close classes in feature space. Experiments on synthetic and real SAR images show that the proposed method behaves well in SAR image segmentation performance.

关键词： Between-cluster variation fuzzy clustering algorithm nonlocal spatial information robust separation measure synthetic aperture radar (SAR) image segmentation

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Analysis and application of martial arts video image based on fuzzy clustering algorithm

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2021年第4期40卷 6339-6347页

作者： Zhao, Chao Yang, Hongling Li, Xiaoqian Li, Rui Zheng, Shoucun Wuhan Sports Univ Sch Martial Arts Wuhan Hubei Peoples R China Dalian Neusoft Univ Informat Daliang Liaoning Peoples R China Hebei Sports Univ Shijiazhuang Hebei Peoples R China Hebei Vocat Coll Polit & Law Dept Phys Educ Shijiazhuang Hebei Peoples R China Cangzhou Normal Univ Cangzhou Peoples R China

The intelligent scheduling algorithm for hierarchical data migration is a key issue in data management. Mass media content platforms and the discovery of content object usage patterns is the basic schedule of data migration. We add QPop, the dimensionality reduction result of media content usage logs, as content objects for discovering usage patterns. On this basis, a clustering algorithm QPop is proposed to increase the time segmentation, thereby improving the mining performance. We hired the standard C-means algorithm as the clustering core and used segmentation to conduct an experimental mining process to collect the ted QPop increments in practical applications. The results show that the improved algorithm has good robustness in cluster cohesion and other indicators, slightly better than the basic model.

关键词： fuzzy clustering algorithm membership martial arts video image segmentation intersection clustering object extraction

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An energy efficiency evaluation method of intelligent building based on fuzzy clustering algorithm

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2023年第4-5期45卷 421-435页

作者： Wang, Shuai Sun, Lei Yuan, Xinjie Xinxiang Vocat & Tech Coll Sch Architectural Engn Xinxiang 453000 Peoples R China

Aiming at the problems of low evaluation accuracy and long evaluation time in the traditional energy-saving evaluation methods of smart buildings, an energy-saving evaluation method of smart buildings based on fuzzy clustering algorithm is proposed. Firstly, according to the construction criteria of the evaluation index system, establish the energy-saving evaluation index system of smart buildings, obtain the evaluation indexes, then use the grey correlation theory to determine the weight of the energy-saving evaluation index of smart buildings, and measure the energy consumption of smart buildings separately. Finally, establish the fuzzy similarity matrix, and cluster with the network method in the direct clustering method to obtain the optimal clustering scheme and evaluate the energy-saving of smart buildings. The simulation results show that the accuracy of the proposed method is 100% and the evaluation time is within 7 s. The evaluation effect of the proposed method is good and the evaluation efficiency is high.

关键词： fuzzy clustering algorithm grey correlation theory smart building energy-saving evaluation evaluation index system

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Classification for electric power companies based on fuzzy clustering algorithm

Classification for electric power companies based on fuzzy c...

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14th International Conference on Management Science and Engineering

作者： Li Jian-feng Chen Yan Zhai Jun Dalian Maritime Univ Coll Econ & Management Dalian 116000 Peoples R China

ISBN: (纸本)9787560322780

fuzzy clustering is an important approach in data mining. It has been applied broadly in many aspects and receiving great attention from enterprisers and scholars. This paper makes use of MATLAB language to produce a fuzzy clustering algorithm for classifying electric power companies according to some general financial indexes at the end of 2006, such as ratio of operating income, ratio of stockholder's equity and current ratio. By constructing and normalizing initial partition matrix, getting fuzzy similar matrix with Minkowski metric and gaining the transitive closure, the dynamic fuzzy clustering analysis for electric power companies is shown clearly that different clustered result change gradually with the threshold X reducing. It has a great value on many things, such as contrasting electric power companies' financial condition in order to grasp the chance of investment. http://***/stamp/***?arnumber=4421911

关键词： fuzzy clustering algorithm electric power company matlab classification

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Attribute Weighted fuzzy clustering algorithm Based on Mutual Information 13

Attribute Weighted Fuzzy Clustering Algorithm Based on Mutua...

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13th International Conference on Natural Computation, fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Cao, Yao Zhu Lin, He Liu, Biao Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

ISBN: (纸本)9781538621653

It is studied by applying the mutual information which is used to assess the contribution of each attribute that has the different important degrees to the classification in the fuzzy clustering algorithm, then the attribute weighted fuzzy clustering algorithm based on mutual information is proposed. By using the mutual information to quantify the contribution of each attribute to the classification, the attributes are weighted and introduced into the fuzzy C mean algorithm. For incomplete data sets, the missing attribute is also introduced as a target object to be optimized and as a part of the iterative to be optimization. Finally, an example verifies the applicability of the algorithm in dealing with incomplete data sets and incomplete data sets, and analyzes the effect of each attribute value loss on clustering results in incomplete data sets.

关键词： Granular Computing fuzzy clustering algorithm Mutual information and attribute weighted Incomplete data sets membership

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A fuzzy clustering algorithm Based on Weighted Index and Optimization of clustering Number

A Fuzzy Clustering Algorithm Based on Weighted Index and Opt...

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8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： Wang, Wen-qi Li, Qiang Air Force Early Warning Acad Wuhan 430000 Hubei Peoples R China

ISBN: (纸本)9783642549243;9783642549236

In view of the discretization of continuous attributes of civil aviation radar intelligence data, this paper proposes a fuzzy partition algorithm of continuous attributes based on weighted index and optimization of clustering number, and its automatic determination of optimal weighted index m* and optimal clustering number c* overcomes the shortcomings of current attribute fuzzy methods of manual determination of classification number and no consideration of geometry data. The experimental results verify the validity and feasibility of fuzzy attribute discretization of civil aviation radar intelligence data characteristics.

关键词： Continuous attributes Weighted index clustering number fuzzy clustering algorithm

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Study on Application of Production Flow Analysis in Manufacturing Enterprise Based on fuzzy clustering algorithm

Study on Application of Production Flow Analysis in Manufact...

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4th International Conference on Machine Vision (ICMV) - Machine Vision, Image Processing and Pattern Analysis

作者： Du Yanwei Xian Technol Univ Sch Mechatron Engn Xian Peoples R China

ISBN: (纸本)9780819490254

Group Technology is a method of increasing the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems in the many variety and small batch production. The parts with the same or similar process route are gathered into a group through production flow analysis. Each part processing route was analyzed. The production flow analysis figure of the parts was drawn. The fuzzy clustering algorithm was used to classify the machine tools and the parts. The fuzzy similar matrices and the relay closure matrices were obtained based on MATLAB. Then the corresponding relations between each group of the parts and each group of the machine tools were found. The similar parts were gathered and the workshop machine layout was reconstructed. The steps of production flow analysis and the fuzzy clustering analysis were introduced. The effectiveness of the method was proved by an example.

关键词： Group technology production flow analysis fuzzy clustering algorithm MATLAB

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Sentence clustering in Text Document Using fuzzy clustering algorithm

Sentence Clustering in Text Document Using Fuzzy Clustering ...

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International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT)

作者： Sruthi, S. Shalini, L. TVM MCET Dept CSE Thiruvananthapuram Kerala India

ISBN: (纸本)9781479941902

The paper proposes FRECCA algorithm to find out the clusters of sentences having the inter relation among them. The system uses the fuzzy clustering algorithm that compares the sentences and find out the similarity value and forms a cluster. The fuzzy clustering algorithm checks the possibility of the sentence to which cluster it belongs to. In this paper a Genetic algorithm approach is also suggested that checks the highest similarity value between the sentences and grouped to form a cluster in a more sophisticated manner, there by forming n number of clusters from n sentences.

关键词： fuzzy clustering algorithm FRECCA algorithm Genetic algorithm

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fuzzy clustering algorithm for university students' psychological fitness and performance detection

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HELIYON 2023年第8期9卷 e18550页

作者： Han, Haiyan Xian Univ Mental Hlth Educ Ctr Coll Students Xian 710065 Peoples R China

Students' psychological fitness is unavoidable, hindering personal development, social interactions, peer influence, and adolescence. Academic stress may be the most dominant factor affecting college students' mental well-being. Therefore, improving the monitoring of mental health issues among college students is a vital topic for study. However, identifying the student's stress level is challenging, leading to uncertainty. Hence, this paper suggests Heuristic fuzzy Cmeans clustering algorithm (HFCA) for analyzing college students' stress levels, psychological well-being and academic performance detection. The data are collected from the Kaggle stress dataset for predicting student mental health. This study investigates the psychological factors affecting students' academic performance using the suggested HFCA. Students' performance may be predicted using the fuzzy Cognitive Map (FCM) in this study. This study used fuzzy clustering algorithms to discover the most crucial aspects of student success, such as student involvement and satisfaction. A better understanding of the risk factors for and protective factors against poor mental health can serve as the basis for developing policies and targeted interventions to prevent mental health problems and guarantee that at-risk students can access the help they need. The experimental analysis shows the proposed method HFCA to achieve a high student performance ratio of 96.7%, cognitive development ratio of 97.2%, student engagement ratio of 97.5% and prediction ratio of 95.1% compared to other methods.

关键词： fuzzy clustering algorithm Students' psychological fitness Academic performance

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