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Remote Sensing Image Classification Based on Improved Fuzzy c-Means

Geo-Spatial Information Science 2008年第2期11卷 90-94页

作者： YU Jie GUO Peihuang CHEN Pinxiang ZHANG Zhongshan RUAN Wenbin School of Remote Sensing and Information Engineering Wuhan University 129 Luoya Road Wuhan 430079 China 不详

Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classifi-cation accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the tradi-tional fcm algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which fcm cannot do. Moreover, they can improve the classification accuracy of remote sensing images.

关键词： fcm algorithm GK algorithm GG algorithm remote sensing classification

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A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership algorithm

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ENERGIES 2018年第1期11卷 238页

作者： Zhao, Qiang Shao, Shuai Lu, Lingxing Liu, Xin Zhu, Honglu North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China North China Elect Power Univ Sch Renewable Energy Beijing 102206 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China

Photovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array's electrical behavior characteristics under fault conditions is analyzed in this paper, and a novel PV array fault diagnosis method is proposed based on fuzzy C-mean (fcm) and fuzzy membership algorithms. Firstly, clustering analysis of PV array fault samples is conducted using the fcm algorithm, indicating that there is a fixed relationship between the distribution characteristics of cluster centers and the different fault, then the fault samples are classified effectively. The membership degrees of all fault data and cluster centers are then determined by the fuzzy membership algorithm for the final fault diagnosis. Simulation analysis indicated that the diagnostic accuracy of the proposed method was 96%. Field experiments further verified the correctness and effectiveness of the proposed method. In this paper, various types of fault distribution features are effectively identified by the fcm algorithm, whether the PV array operation parameters belong to the fault category is determined by fuzzy membership algorithm, and the advantage of the proposed method is it can classify the fault data from normal operating data without foreknowledge.

关键词： PV array fcm algorithm cluster analysis fault diagnosis membership algorithm

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From Type 1 to Full Type N Fuzzy System Models

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JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING 2014年第4-6期22卷 543-560页

作者： Turksen, I. Burhan TOBB Econ & Technol Univ Dept Ind Engn TR-06560 Ankara Turkey Univ Toronto Dept Mech & Ind Engn Knowledge Intelligence Syst Lab Toronto ON M5S 3G8 Canada

We first brief review the essential Type 1 Fuzzy System models. Next we state the well-known fcm algorithm which lets one to extract Type 1 membership values from a given data set for the development of Type 1 fuzzy system models as a foundation for the development of Full Type 2 fuzzy system models. For this purpose, we describe how one can generate Full Type 2 membership value distributions for a development of second order fuzzy system models with our proposed second order data analysis. We also suggest that a recursive restatement of fcm algorithm can allow the generation of Full Type 3 and ... Type n fuzzy system models if one were to investigate such system models in the future. We present our results graphicallyfor TD_Stockprice data with respect to three validity indeces: 1)Xie-Beni's, 2)Celikyilmaz-Turksen's and 3)Bezdek's.

关键词： Zadeh's rulebase model Takagi and Sugeno's model Turksen's fuzzy regression model fcm algorithm Type 1 fuzzy system models Full Type 2 fuzzy system models TD_Stockprice data Celikyilmaz-Turksen Bezdek indeces

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An Intelligent Optimization Clustering algorithm for Driver Risk Tendency Recognition in Intelligent Transportation Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年

作者： Ma, Ning Wu, Kaijun Yuan, Yubin Liu, Shuoxun Li, Jiawei Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China Tianjin Yingbeite Aerosp Technol Co Ltd Tianjin 300308 Peoples R China

Driving behavior assessment is of great significance in intelligent transportation systems to improve road safety and traffic efficiency. This paper proposes a novel weighted fuzzy C-means (fcm) clustering algorithm, called GSA-Wfcm, which combines a genetic algorithm and a simulated annealing algorithm to optimize the evaluation process of driving behavior. GSA-Wfcm first uses a genetic algorithm to optimize the clustering center to ensure the diversity and effectiveness of the initial selection;then, it uses a simulated annealing algorithm to avoid local optimal solutions, thereby improving the global nature of the clustering results. In order to enhance the robustness and accuracy of clustering, the algorithm introduces a point density weighting mechanism to improve the algorithm's adaptability to data features of different dimensions. By integrating the advantages of genetic algorithms and simulated annealing algorithms, the algorithm achieves intelligent initialization, optimizes the initial clustering center, and significantly improves the convergence speed and stability of the algorithm. Experimental results on multiple data sets show that GSA-Wfcm effectively overcomes the shortcomings of traditional fcm in initial cluster center selection, showing faster convergence and stronger robustness. Further verification on traffic data sets shows that the algorithm performs well in identifying different types of driving behaviors and can effectively classify cautious driving, normal driving and aggressive driving. The accuracy and stability of clustering are significantly improved, and the clustering performance reaches 0.81, providing a reliable tool for driving behavior analysis in intelligent transportation systems.

关键词： Clustering algorithms Heuristic algorithms Classification algorithms Accuracy Simulated annealing Genetic algorithms Machine learning algorithms Vehicles Sensitivity Robustness genetic algorithm fcm algorithm weighted clustering driver behavior assessment

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Location Optimization Model of Regional Express Distribution Center

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Procedia - Social and Behavioral Sciences 2013年 96卷 1008-1013页

作者： Yingfeng Ji Hualong Yang Yan Zhang Weixin Zhong Transportation Management College Dalian Maritime University Dalian China

As e-business grows rapidly in recent years, express delivery industry ushered in the unprecedented opportunities for development. This also resulted in a fierce competition within the express delivery industry. How to reduce logistics cost and serve customer better has become the focus of many express companies. The importance of express distribution center and the design of its location have aroused extensive attention. Therefore, the location optimization problem of express distribution center has been paid much more attention both in industry and academic world. This paper established an optimization model of regional express distribution center location based on the reality of the road network. The Fuzzy C Means (fcm) clustering algorithm was improved to solve the model. An empirical study was implemented via a case, and it is validated that the established model and its algorithm are feasible.

关键词： express distribution distribution center: location optimization fcm algorithm

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Analysis for Customer Lifetime Value Categorization with RFM Model

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Procedia Computer Science 2019年 161卷 834-840页

作者： Siti Monalisa Putri Nadya Rice Novita UIN Suska Riau HR.Soebrantas street Pekanbaru and 28293 Indonesia

The same treatment of all customers will cause customers who are not so valuable will be value destroyer rather than a value creator. Giving discounts and promos to all customers has not provided benefits to a company in retaining customers. These two things are currently being experienced by LWC Company so that changes are needed in evaluating the strategy taken to keep the relationship with customers, especially potential customers, by forming customer portfolios. Customers portfolios can be analyzed from customer transactions. With customer transactions data, the company can find out which potential customers and the customers who do give less value for the company. The way to analyze customer transaction data is by clustering customers with Fuzzy C-means algorithm by using RFM model. Forming the number of clusters is validated by PCI model and ranking is done by multiplying AHP weight to find the life value of the customer so that it can be known which customer group gives high value to the company. The result of this research is that the customers of LWC Company categorized as superstar customer, typical customer and dormant customer. Based on the portfolio, LWC Company can carry out strategy in managing their customer according to the type of the portfolio.

关键词： fcm algorithm PCI AHP CLV Categorization

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Research on design of customer portrait system for E-commerce

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第2期8卷 275-288页

作者： Cai, Lingjie Li, Jun Hubei Univ Technol Engn & Technol Coll Wuhan 430068 Hubei Peoples R China

Customer portrait is the customer information that is labelled and digitised. Collecting and studying consumer data is highly helpful to enterprises because it empowers them to use these data to locate target groups and meet various customer needs. With the continuous rise of the e-commerce market, major e-commerce websites list providing customer value and being data-driven as company values. Traditional customer portraits rely excessively on objective and past experience;so, a simple but powerful customer portrait system is required to make the customer analysis platform simple and intelligent, and to make the data accumulated at ordinary times bring practical value to e-commerce companies. Based mainly on addressing this requirement, this paper proposes an effective customer portrait system for e-commerce. First, the research background and significance is expounded, which lays a theoretical foundation for this research. Then, the customer portrait system is designed, including the overall design scheme and functional framework of the system. The improved fcm algorithm is used as the basis for building the customer portrait model, and the performance of this algorithm is analysed and compared with that of the improved algorithm used in this paper.

关键词： customer portrait system fcm algorithm cluster analysis

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Research on the practice of innovation and entrepreneurship education in universities under the environment of big data

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Wang, Haijun Hunan High speed Railway Vocat Technol Coll Hengyang 421002 Hunan Peoples R China

Entrepreneurship education is an educational concept and teaching mode which is formed in socio-economic development. Universal entrepreneurship education has both the long-term value of realizing the transformation and upgrading of industrial structure and the practical value of alleviating the employment problem of college students. Therefore, how establishing the innovation and entrepreneurship education model in higher education under the environment of big data analysis is a major issue at present and also a key element to achieving the construction goal of modern higher education. In this paper, the entrepreneurship data of colleges and universities are deeply mined by association rules of the Apriori algorithm and clustering analysis of the fcm algorithm. As a result, an entrepreneurship education model is established and practiced in university classrooms. According to the results of the survey after the practice of the entrepreneurship education model, it can be seen that the number of students' participation in innovation and entrepreneurship lectures has increased, and the proportion reached 50%. Innovation and entrepreneurship clubs formed on campus and discipline competitions accounted for 16.00% and 18.00%, respectively. College students' motivations for learning innovation and entrepreneurship education courses are mainly ability enhancement, interest-driven, credit demand, future planning, and influence of others, and they account for 34.00%, 26.00%, 21.00%, 14.00%, and 5.00%, in that order. These indicate that the current group of college students considers the pursuit of knowledge and the improvement of their abilities as the main reasons for undertaking entrepreneurial learning activities. After practicing the entrepreneurship education model, people have increased their knowledge of entrepreneurship theory and are more willing to participate in various entrepreneurial activities.

关键词： Data mining Apriori algorithm fcm algorithm Innovation and entrepreneurship Educational practice model

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Research on the innovation theory of college students' ideological and political education under the background of big data

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期9卷

作者： Lai, Wenling Wuchang Univ Technol Tongshi Coll Qual Educ Wuhan 430223 Hubei Peoples R China

In order to improve the ideological and political education of college students, this paper constructs a cloud technology education platform based on big data analysis and collection technology. Firstly, the collaborative filtering algorithm is used to filter and collect student information;secondly, the Pearson correlation formula is used for pre-processing, MAE (mean absolute error value) is used as an evaluation index;and finally, the data is combined with fcm (fuzzy C-mean) algorithm to filter and analyze as needed. Its application performance is examined to verify the rationality and practicality of the construction of the cloud technology education platform. The analysis results show that, compared with the three-tier neural network education platform and MOOC (Massive Open Online Course) education platform, the educational resource transmission time of the cloud technology education platform constructed in this paper is reduced by 1/3, and the load balancing deviation is reduced by 1/4. Among four different servers, the average error value of the platform constructed in this paper is as low as 0.35% and can reach as high as 0.65%. The detection rate reaches 97.64%, the false alarm rate is only 2.93%, and the leakage rate is only 1.13%. It can be seen that the cloud technology education platform constructed in this paper can improve the utilization rate of educational resources and realize the sharing of educational resources.

关键词： Big data collection and analysis technology Cloud technology education platform fcm algorithm Collaborative filtering algorithm Pearson's correlation calculation formula

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Design and Implementation of Office Management System in Small and Medium Sized Enterprises based on Data Mining Technology

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International Journal of Technology Management 2014年第8期 128-130页

作者： Qiangfei Yin Qiu Li Jiangxi Modem Polytechnic College Nanchang City Jiangxi Province330095 China Jiangxi Modem Technicial Training Collge Nanchang City Jiangxi Province330028 China

This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the fcm algorithm and principle and described implementation steps, and proposed the improvement fcm algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved fcm algorithm is applied in the system.

关键词： Enterprise competitive intelligence system K means algorithm fcm algorithm

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