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Yiming Tang, Rui Chen and Bowen Xia
Nowadays, most fuzzy clustering algorithms are sensitive to the initialization results of clustering algorithms and have a weak ability to handle high-dimensional data. To solve these problems, we developed the viewpoint-driven subspace fuzzy c-means (VS...
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Shiyuan Zhu, Yuwei Zhao and Shihong Yue
Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data obje...
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Kaisheng Zhang, Daniel (Jian) Sun, Suwan Shen, Yi Zhu
With the development of in-vehicle data collection devices, GPS trajectory has become a priority source to identify traffic congestion and understand the operational states of road network in recent years. This study aims to investigate the relationship...
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Amit Banerjee and Issam Abu-Mahfouz
Fuzzy c-means (FCM), the fuzzy variant of the popular k-means, has been used for data clustering when cluster boundaries are not well defined. The choice of initial cluster prototypes (or the initialization of cluster memberships), and the fact that the ...
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Randall W. Gentry
Pág. Page:464 - 479Abstrac
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Md Shahariar Alam, Md Mahbubur Rahman, Mohammad Amazad Hossain, Md Khairul Islam, Kazi Mowdud Ahmed, Khandaker Takdir Ahmed, Bikash Chandra Singh and Md Sipon Miah
In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human b...
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In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human b...
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Mahshid Khazaeiathar, Reza Hadizadeh, Nasrin Fathollahzadeh Attar and Britta Schmalz
The behavior of hydrological processes is periodic and stochastic due to the influence of climatic factors. Therefore, it is crucial to develop the models based on their periodicity and stochastic nature for prediction. Furthermore, forecasting the strea...
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Xian Li, Shuhe Zhao, Hong Yang, Dianmin Cong and Zhaohua Zhang
Land use and cover change (LUCC) is important for the global biogeochemical cycle and ecosystem. This paper introduced a change detection method based on a bi-band binary mask and an improved fuzzy c-means algorithm to research the LUCC. First, the bi-ba...
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Everton Coimbra de Araújo, Jerry A. Johann, Miguel A. Uribe-Opazo, Eduardo C.G. Camargo
Pág. 617 - 627
This study aimed to apply an approach based on fuzzy clustering for the classification of areas associated with soybean yield combined with the following agrometeorological variables: rainfall, average air temperature and average global solar radiation. ...
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Zhi Quan, Hailong Zhang, Jiyu Luo and Haijun Sun
Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR)...
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Tran Dinh Khang, Manh-Kien Tran and Michael Fowler
Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering...
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Jianjie Sun, Xi Chen, Zhengwu Fu and Giuseppe Lacidogna
In this study, the clustering method of the concrete matrix rupture and rubber fracture damages as well as the prediction of the ultimate load of crumb rubber concrete using the acoustic emission (AE) technique were investigated. The loading environment ...
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Ligang Yuan, Yang Zeng, Haiyan Chen and Jiazhi Jin
In order to quantify the degree of influence of weather on traffic situations in real time, this paper proposes a terminal traffic situation prediction model under the influence of weather (TSPM-W) based on deep learning approaches. First, a feature set ...
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Bing Yang, Sensen Wu and Zhen Yan
Food security has been one of the greatest global concerns facing the current complicated situation. Among these, the impact of climate change on agricultural production is dynamic over time and space, making it a major challenge to food security. Taking...
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Raffaele Albano
Three-dimensional (3D) reconstruction techniques are increasingly used to obtain 3D representations of buildings due to the broad range of applications for 3D city models related to sustainability, efficiency and resilience (i.e., energy demand estimatio...
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Yevgeniy Bodyanskiy, Iryna Perova, Polina Zhernova
Pág. 16 - 24
The subject matter of the article is fuzzy clustering of high-dimensional data based on the ensemble approach, provided that a number and shape of clusters are not known. The goal of the work is to create the neuro-fuzzy approach for clustering data when...
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