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A novel cluster validity index for fuzzy clustering based on bipartite modularity

FUZZY SETS AND SYSTEMS 2014年 253卷 122-137页

作者： Zhang, Dawei Ji, Min Yang, Jun Zhang, Yong Xie, Fuding Liaoning Normal Univ Sch Comp Sci & Technol Dalian 116081 Liaoning Peoples R China Liaoning Normal Univ Sch Urban & Environm Sci Dalian 116029 Liaoning Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100080 Peoples R China

A novel cluster validity index whose implementation is based on the membership degrees and improved bipartite modularity of bipartite network is proposed for the validation of partitions produced by the fuzzy c-means (fcm) algorithm. fcm algorithm is employed to group the dataset in order to obtain the membership degree of samples. Then, a weighted bipartite network is constructed by samples and centroids of each cluster. This allows the introduction of a new measurement for optimizing the numbers of clusters for fuzzy partitions. The proposed index utilizes the optimum membership as its global property and the modularity of bipartite network as its local independent property. The proposed index is compared with a number of popular validation indices on fifteen datasets. The experimental results show that the effectiveness and reliability of the proposal is superior to other indices. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Fuzzy clustering Cluster validity index Bipartite modularity fcm algorithm

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A method for classifying citrus surface defects based on machine vision

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021年第3期15卷 2877-2888页

作者： Zhang, Wenzhuo Tan, Aijiao Zhou, Guoxiong Chen, Aibin Li, Mingxuan Chen, Xiao He, Mingfang Hu, Yahui Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha Peoples R China Cent South Univ Forestry & Technol Hunan Prov Key Lab Urban Forest Ecol Changsha Peoples R China Hunan Acad Agr Sci Inst Plant Protect Changsha Hunan Peoples R China

When detecting citrus surface defects, the performance of machine vision system is affected by different aspects such as the size, shape and environment. Therefore, a method for classifying citrus surface defects based on machine vision was proposed in this paper. First, the Fuzzy C-Means algorithm optimized by the Gray Wolf Optimizer algorithm was used to preprocess the citrus image. The citrus in the image was separated from the background;Then, the improved convolutional neural network combined with the State Transfer algorithm (STA) was used to identify the citrus surface defects. We selected 2000 Tribute Citrus, 1000 ones with and without the defects separately, to carry on the experiment. The identification accuracy of the trained model on the dataset was 99.1%. In order to verify the effectiveness of the model in complex background, the convolutional neural network in combination with a STA was compared with SVM, AlexNet, VGG16 and other methods. The experimental results show that the citrus surface defect classification method based on machine vision is effective.

关键词： Citrus surface defects Convolutional neural network Machine vision fcm algorithm GWO algorithm State Transition algorithm

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Adaptive learning algorithm based price prediction model for auction lots-deep clustering based interval quoting

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2412-e2412页

作者： Ke, Da Fan, Xianhua Asif, Muhammad Huazhong Univ Sci & Technol Sch Management Wuhan Hubei Peoples R China China Univ Geosci Sch Econ & Management Wuhan Hubei Peoples R China Natl Text Univ Dept Comp Sci Faisalabad Punjab Pakistan

This article addresses the problem of interval pricing for auction items by constructing an auction item price prediction model based on an adaptive learning algorithm. Firstly, considering the confusing class characteristics of auction item prices, a dynamic interclass distance adaptive learning model is developed to identify confusing classes by calculating the differences in prediction values across multiple classifiers for target domain samples. The difference in the predicted values of the target domain samples on multiple classifiers is used to calculate the classification distance, distinguish the confusing classes, and make the similar samples in the target domain more clustered. Secondly, a deep clustering algorithm is constructed, which integrates the temporal characteristics and numerical differences of auction item prices, using DTW-K-medoids based dynamic time warping (DTW) and fuzzy C-means (fcm) algorithms for fine clustering. Finally, the KF-LSTM auction item interval price prediction model is constructed using long short-term memory (LSTM) and dual clustering. Experimental results show that the proposed KF-LSTM model significantly improves the prediction accuracy of auction item prices during fluctuation periods, with an average accuracy rate of 90.23% and an average MAPE of only 5.41%. Additionally, under confidence levels of 80%, 85%, and 90%, the KF-LSTM model achieves an interval coverage rate of over 85% for actual auction item prices, significantly enhancing the accuracy of auction item price predictions. This experiment demonstrates the stability and accuracy of the proposed model when applied to different sets of auction items, providing a valuable reference for research in the auction item price prediction field.

关键词： Adaptive learning algorithm Dual clustering LSTM fcm algorithm Interval price prediction

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CHINESE JOURNAL OF ELECTRONICS 2013年第3期22卷 461-465页

作者： Chen Donghui Liu Zhijing Wang Zonghu Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Traditional fuzzy clustering algorithms based on objective function is unable to determine the optimum number of clusters, sensitive to the initial cluster centers, and easily sunk into the issue of local optimum. A Fuzzy similarity-based clustering (FSBC) algorithm is proposed in this paper. This method consists three phases: first, the objective function is modified by integrating Fuzzy C-means (fcm) and Possibilistic C-means (PCM) method;second, using the density function from data for similarity-based clustering to automatically generate initial prototype without requesting users to specify;finally, the iteration process optimized by Particle swarm optimization (PSO) to obtain appropriate adjustment parameters that can provide better results, which avoids the local minimum problems of traditional methods. The experimental results on the synthetic data and UCI standard data sets show that the proposed algorithm has greater searching capability, less computational complexity, higher clustering precision.

关键词： Fuzzy clustering fcm algorithm PCM algorithm Mountain function Density function PSO algorithm

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A segmentation method for noisy photoelectric image

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OPTIK 2013年第20期124卷 4092-4094页

作者： Qiao, Naosheng Zou, Beiji Hunan Univ Arts & Sci Sch Phys & Elect Changde 415000 Hunan Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410073 Hunan Peoples R China

In order to improve the ability of noisy photoelectric image segmentation and satisfy the requirement of human visual apperception, a noisy photoelectric image segmentation method is proposed in this paper. Firstly, the basic principle of fcm algorithm is analyzed in detail. Secondly, on the basis of PCM algorithm, only such pixels affected by noise lesser that centralized hi the diagonal area of two-dimensional histogram is actualized image processing, and the probability of the pixel that lie in such area is obtained. Thirdly, objective function, membership matrix and cluster centers are renovated on the basis of PCM algorithm. Finally, experiment contrast between our method and other methods is executed, the results of experiment indicate that our method has relatively better segmentation quality and faster segmentation speed. (c) 2013 Elsevier GmbH. All rights reserved.

关键词： Noisy photoelectric image segmentation fcm algorithm Human visual apperception Segmentation coefficient Segmentation entropy

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Big Data Analysis Technology for Electric Vehicle Networks in Smart Cities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第3期22卷 1807-1816页

作者： Lv, Zhihan Qiao, Liang Cai, Ken Wang, Qingjun Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510408 Peoples R China Shenyang Aerosp Univ Sch Econ & Management Shenyang 110136 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211100 Peoples R China

To explore the electric vehicle networks in smart cities through big data analysis technology, this study utilizes K-means and fuzzy theory in big data analysis technology to construct an objective function-based fuzzy mean clustering algorithm theory (fcm). Then, the fcm algorithm is improved, and the electric vehicle network is simulated. The results show that in the analysis of network data transmission performance, when the probability of successful propagation is 100% and the lambda value is between 0.01-0.05, it is closest to the actual result, and the data delay is the smallest. In the analysis of the route guidance effects, when facing congested road sections, the route guidance strategy of this study can restrain the spread of congestion effectively and achieve timely evacuation of traffic congestion. In the further analysis of the impact of different factors on traffic conditions, under route guidance, with the increase in market penetration rate (MPR) of devices, following rate (FR) of vehicles, and congestion level (CL), the improvement of the induction strategy becomes clearer, and greater economic benefits are achieved. This study has found that utilizing big data analysis technology to improve the electric vehicle transportation networks can reduce the network data transmission performance delay significantly and change the path to suppress the spread of congestion effectively, which has provided experimental references for the development of electric vehicle transportation networks.

关键词： Electric vehicle big data analysis fcm algorithm path guidance smart cities

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Effective convolution method for privacy preserving in cloud over big data using map reduce framework

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IET SOFTWARE 2019年第3期13卷 187-194页

作者： Sudhakar, Katherapaka Farquad, M. A. H. Narshimha, G. Univ JNTUH Hyderabad Telangana India Islamic Univ Madinah Fac Comp & Informat Syst Medina Saudi Arabia JNTUH Karimnagar Telangana India

Cloud computing is used to connect several number of remote servers through Internet to accumulate and recover large data anywhere and anytime. As of the conventional privacy defending process, there is a possibility for malevolent assault on the sensitive information accumulated in the cloud. In this research, the authors have proposed a competent large data convert among privacy defending by Hadoop map reduce in the cloud. The procedure exploits fuzzy C-means clustering (fcm) algorithm grouping the data. For dimensionality reduction, map reduce framework will be used. In evaluation module, the recommended technique performed with the aid of K-nearest neighbour (KNN) classification algorithm in this phase using KNN technique to check the convolution process based on the threshold value, which is improving the utility of the privacy data. The consequence acquired illustrates that authors' proposed scheme has enhanced the clustering exactness and also accomplishes the effectual convolution procedure to improve the privacy. From the experimental results, the proposed research achieved an effective clustering accuracy 76.07% and the existing K-means approach gets the clustering accuracy of 73.5% which is minimum value when compared to the proposed researches. The suggested technique is implemented in JAVA with Cloud Sim platform.

关键词： data privacy pattern clustering Big Data parallel processing fuzzy set theory cloud computing security of data pattern classification nearest neighbour methods sensitive information Hadoop map dimensionality reduction evaluation module recommended technique KNN technique convolution process privacy data clustering exactness effectual convolution procedure effective clustering accuracy Cloud Sim platform effective convolution method big data remote servers conventional privacy defending process malevolent assault fcm algorithm K-means approach cloud computing privacy preserving map reduce framework Internet fuzzy C-means clustering algorithm K-nearest neighbour classification algorithm threshold value JAVA

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Urban expressway parallel pattern recognition based on intelligent IOT data processing for smart city

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COMPUTER COMMUNICATIONS 2020年 155卷 40-47页

作者： Liu, Zhendong Jia, Hongfei Wang, Yanxia Jilin Univ Sch Transportat Changchun 130000 Peoples R China Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China

With the sustained and rapid development of the social economy and the rapid growth of urban vehicles, urban expressway has developed rapidly. As the roadmap of urban traffic, the urban expressway has a relatively high and stable driving speed, and also bears a large amount of urban traffic. However, in recent years, with the expansion of the city scale, the congestion of urban expressways has become increasingly severe. In various areas of the merged area, due to various factors such as mismatched traffic capacity and reduced driving speed, it is easy to cause deterioration of road conditions in a short period of time and cause more secondary accidents. In order to reduce the incidence of expressway traffic accidents and to avoid as much as possible the casualties and property losses caused by accidents, Intelligent Transportation Systems (ITS) supported by information technology, data communication transmission technology, control technology and traffic engineering were introduced. With the rise of artificial intelligence and the continuous development of ITS, the video acquisition methods of real-time traffic flow data and the image recognition ability of video sequences continue to improve, providing theoretical basis and technical support for the research of urban expressway parallel pattern recognition. Aiming at the shortcomings of traditional pattern recognition methods, such as weak anti-interference to complex traffic environment and low correct recognition rate, this paper studies the pattern recognition method based on image processing, and selects the fuzzy C-means clustering in the currently used clustering methods (fcm) algorithm Because the fcm algorithm cannot obtain the global optimal solution and need to determine the number of cluster categories in advance, this paper uses ReliefF algorithm and Particle Swarm Optimization (PSO) to compare the feature weight and number of clusters of traditional fcm algorithm. Make improvements. Through the experi

关键词： Intelligent transportation system Pattern recognition Image processing fcm algorithm

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Cost-effective land cover classification for remote sensing images

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-12页

作者： Li, Dongwei Wang, Shuliang He, Qiang Yang, Yun Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Swinburne Univ Technol Dept Comp Technol Melbourne Vic Australia

Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs tremendous computational resources, which often becomes a huge burden to the remote sensing community. Undoubtedly cloud computing is one of the best choices for land cover classification, however, if not managed properly, the computation cost on the cloud could be surprisingly high. Recently, cutting the unnecessary computation long tail has become a promising solution for saving cost in the cloud. For land cover classification, it is generally not necessary to achieve the best accuracy and 85% can be regarded as a reliable land cover classification. Therefore, in this paper, we propose a framework for cost-effective remote sensing classification. Given the desired accuracy, the clustering algorithm can stop early for cost-saving whilst achieving sufficient accuracy for land cover image classification. Experimental results show that achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud computation cost for achieving a 100% accuracy. To put it into perspective, for the US land cover classification example, the proposed approach can save over $1,593,490.18 for the government in each single-use when the desired accuracy is 90%.

关键词： Remote sensing Land cover classification Cloud computing fcm algorithm

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Suppressed fuzzy C-means clustering algorithm

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PATTERN RECOGNITION LETTERS 2003年第9-10期24卷 1607-1612页

作者： Fan, JL Zhen, WZ Xie, WX Xian Inst Post & Telecommun Dept Informat & Control Xian 710061 Shaanxi Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Shenzhen Univ Sch Informat Engn Shenzhen 518060 Peoples R China

Based on the defect of rival checked fuzzy c-means clustering algorithm, a new algorithm: suppressed fuzzy c-means clustering algorithm is proposed. The new algorithm overcomes the shortcomings of the original algorithm, establishes more natural and more reasonable relationships between hard c-means clustering algorithm and fuzzy c-means clustering algorithm. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： fuzzy partition HCM algorithm fcm algorithm

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