Search Results - dynamic fuzzy c‐means algorithm

Modified dynamic fuzzy c-means clustering algorithm – Application in dynamic customer segmentation by Munusamy, Sivaguru, Murugesan, Punniyamoorthy

“… The Crespo’s dynamic fuzzy c -means (CDFCM) is one of the clustering algorithms introduced in the literature for DCS…”
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Dynamic customer segmentation: a case study using the modified dynamic fuzzy c-means clustering algorithm by Sivaguru, M.

“… To improve the effectiveness of the proposed framework, the existing dynamic fuzzy c -means clustering (dFCM…”
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Cloud Workload Forecasting via Latency‐Aware Time Series Clustering‐Based Scheduling Technique by Sridhar, P., Sathiya, R. R.

“… To overcome these drawbacks, a novel latency‐aware time series‐based scheduling (LATS) algorithm has been proposed in this paper for predicting future server loads…”
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Retracted: A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video by Engineering, Mathematical Problems in

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A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video by Zhou, Yonghua, Huang, Ting, Li, Shangbin

“… To solve this problem, a soccer detection algorithm was based on class weighted spatial Fuzzy C-means (ws-FCM) was proposed…”
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Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means by Awad, M., Chehdi, K., Nasri, A.

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An image segmentation approach based on fuzzy c-means and dynamic particle swarm optimization algorithm by Dhanachandra, Nameirakpam, Chanu, Yambem Jina

“…Image segmentation has considered an important step in image processing. Fuzzy c-means (FCM) is one of the commonly used clustering algorithms because of its simplicity and effectiveness…”
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An Enhanced Interval Type-2 Fuzzy C-Means Algorithm for Fuzzy Time Series Forecasting of Vegetation Dynamics: A Case Study from the Aksu Region, Xinjiang, China by Chen, Yongqi, Liu, Li, Cao, Jinhua, Wang, Kexin, Li, Shengyang, Yin, Yue

“…) prediction models based on the Fuzzy C-Means (FCM) clustering algorithm address some of these uncertainties by enabling soft partitioning through membership functions…”
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Research on state prediction of secondary device in smart substation based on dynamic interval weighted fuzzy c-means algorithm by Zhongyang, Chen, Pengfei, Zhang, Mahuan

“…A weighted fuzzy clustering algorithm based on dynamic interval is proposed to solve the problem of inaccuracy of missing attributes in clustering…”
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Improved Fuzzy C-Means Clustering Algorithm-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Features in the Diagnosis of Invasive Breast Carcinoma before and after Menopause by Mei, Li, Wang, Kaixiang, Gu, Yongjian

“…The application effect of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on the improved fuzzy C-mean clustering…”
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Dynamic iterative imputation algorithm based on rough fuzzy C-Means clustering by Gong, Zheng, Yan, Chun, Liu, Wei

“…Missing data is a very common phenomenon in daily life. To mitigate its adverse impact on data analysis, this paper proposes a dynamic iterative imputation algorithm based on the rough fuzzy C-means algorithm…”
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Solving dynamic multi-objective engineering design problems via fuzzy c-means prediction algorithm by Zhang, Qingyang, Fu, Xueliang, Yang, Shengxiang, Jiang, Shouyong, Li, Miqing, Zheng, Zedong

“…This paper proposes a new prediction algorithm by integrating the fuzzy c-means and regression analysis fitting techniques with multi-objective differential evolution (FRMODE…”
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A Dynamic QoS Mapping Algorithm for 5G-TSN Converged Networks Based on Weighted Fuzzy C-Means and Three-Way Decision Theory by Wu, Yuhang, Xu, Fangmin, Ning, Lina, Liu, Xiaokai, Chen, Hongyuan, Lu, Xingbo, Zhao, Chenglin

“…To ensure end-to-end Quality of Service (QoS) management in 5G-TSN converged networks, this paper proposes a dynamic weighted QoS mapping method based on Weighted Fuzzy C-Means and Three-Way Decisions (WFCM-TDwQM…”
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Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means by Awad, M, Chehdi, K, Nasri, A

“… of imperfections induced by the process of image acquisition. Recently, fuzzy C-means (FCM) and Genetic Algorithms were separately used in segmenting multi-component images but neither of them had successfully addressed the above…”
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A Novel Dynamic Fingerprint Segmentation Method Based on Fuzzy C-Means and Genetic Algorithm by Lei, Wu, Lin, You

“… The proposed algorithm is based on the existed dynamic image segmentation algorithm using fuzzy c-means (FCM…”
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Novel validity indices for dynamic clustering and an Improved Dynamic Fuzzy C-Means by Saltos, Ramiro, Carvajal, Ignacio, Crespo, Fernando, Weber, Richard

“…Dynamic clustering algorithms play a crucial role in numerous real-world applications by continuously adapting to evolving data patterns and identifying changes within the underlying cluster structure…”
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Dynamic Background Subtraction in Video Surveillance Using Color-Histogram and Fuzzy C-Means Algorithm with Cosine Similarity by Yasir, Maryam A., Yossra Hussain Ali

“… In this paper, we propose a model of pixel-based color-histogram and Fuzzy C-means (FCM) to obtain the background model using cosine similarity (CS…”
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Machine learning based centralized dynamic clustering algorithm for energy efficient routing in vehicular ad hoc networks by Choksi, Amit, Shah, Mehul

“…Vehicular ad hoc network (VANET) is a dynamic and self‐configure wireless network that connects vehicles to provide…”
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A novel Move-Split-Merge based Fuzzy C-Means algorithm for clustering time series by Ba, Wei, Gu, Zongquan

“… In order to cope with the challenge of noisy time series data and to improve the performance of clustering algorithms, a Move-Split-Merge based Fuzzy C-Means algorithm (MSMFCM) is proposed…”
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Incremental Fuzzy Clustering-Based Neural Networks Driven With the Aid of Dynamic Input Space Partition and Quasi-Fuzzy Local Models by Zhang, Congcong, Oh, Sung-Kwun, Fu, Zunwei, Pedrycz, Witold

“… Specifically, we employ dynamic (incremental) fuzzy C-means (FCMs) clustering algorithms to reveal a structure in data and divide the entire input space into several subregions…”
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