Suchergebnisse - self adaptive clustering algorithm

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks von Pratha, Sebasthiyar Jaya, Asanambigai, Valayapathy, Mugunthan, Seenapuram Rajan

“… In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search …”
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Research on self-adaptive clustering algorithms for large data sparse networks based on information entropy von Ma, Tingting, Zhang, Guanhong

“… However, so many data are unnecessary in real life. The application of data clustering algorithm based on artificial intelligence technology can solve such problems very well …”
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A self-adaptive density-based clustering algorithm for varying densities datasets with strong disturbance factor von Cai, Zihao, Gu, Zhaodong, He, Kejing

“… image analysis, text mining, and anomaly detection. DBSCAN is a powerful tool for clustering analysis and is widely used in density-based clustering algorithms …”
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Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning von Miao, Jiaqing, Zhou, Xiaobing, Huang, Ting-Zhu

“… Image segmentation is an active research topic in image processing. The Fuzzy C-means (FCM) clustering analysis has been widely used in image segmentation …”
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Dynamic noise self-recovery ECM clustering algorithm with adaptive spatial constraints for image segmentation von Lan, Rong, Wang, Bo, Yu, Xiaoying, Zhao, Feng, Mi, Haowen, Yu, Haiyan, Zhang, Lu

“… To address these problems, we propose a dynamic noise self-recovery ECM clustering algorithm with adaptive spatial constraints for image segmentation …”
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SAKM: Self-adaptive kernel machine A kernel-based algorithm for online clustering von Amadou Boubacar, Habiboulaye, Lecoeuche, Stéphane, Maouche, Salah

“… This paper presents a new online clustering algorithm called SAKM (Self-Adaptive Kernel Machine …”
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SAMNET: Self-adaptative multi-kernel clustering algorithm for urban VANETs von Sellami, Lamaa, Alaya, Bechir

“… However, according to recent studies, the adoption of an efficient VANET clustering algorithm can promote road safety, provide a means of entertainment for passengers, and promote message routing …”
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A Novel Self-Adaptive Affinity Propagation Clustering Algorithm Based on Density Peak Theory and Weighted Similarity von Wang, Limin, Hao, Zhiyuan, Sun, Wenjing

“… ), the self-adaptive affinity propagation clustering algorithm based on density peak clustering and weighted similarity (DPWSAP) was proposed …”
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A self-training subspace clustering algorithm based on adaptive confidence for gene expression data von Li, Dan, Liang, Hongnan, Qin, Pan, Wang, Jia

“… To solve the problem, this paper proposes a self-training subspace clustering algorithm based on adaptive confidence for gene expression data (SSCAC …”
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A Novel, Self-Adaptive, Multiclass Priority Algorithm with VM Clustering for Efficient Cloud Resource Allocation von Ben Alla, Hicham, Ben Alla, Said, Ezzati, Abdellah, Touhafi, Abdellah

“… To address these challenges in cloud computing environments, particularly within the infrastructure of service models, we propose a novel, self-adaptive, multiclass priority algorithm with VM …”
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Deep Subspace Clustering Algorithm with Data Augmentation and Adaptive Self-Paced Learning von Yuyan JIANG, Chengfeng TAO, Ping LI

“… Deep subspace clustering achieves better performance than traditional clustering by jointly performing self-expressed feature learning and cluster …”
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Hybrid fuzzy clustering methods based on improved self-adaptive cellular genetic algorithm and optimal-selection-based fuzzy c-means von Jie, Lilin, Liu, Weidong, Sun, Zheng, Teng, Shasha

“… •An improved self-adaptive cellular genetic algorithm (IDCGA) is presented for a more efficient search by combining the Arnold cat map with modified evolution rule, as well …”
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Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering von Lu, Yang, Cheung, Yiu-Ming, Tang, Yuan Yan

“… >-means-type competitive learning. We introduce a new method called self-adaptive multiprototype-based competitive learning (SMCL …”
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Self-Adaptive Genetic Algorithm for Clustering von Kivijärvi, Juha, Fränti, Pasi, Nevalainen, Olli

“… We introduce a new self-adaptive GA that finds the parameter setup on-line during the execution of the algorithm …”
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A self-adaptive evolutionary algorithm using Monte Carlo Fragment insertion and conformation clustering for the protein structure prediction problem von Parpinelli, Rafael Stubs, Will, Nilcimar Neitzel, da Silva, Renan Samuel

“… As such, this work has as its primary goal to improve the prediction power of ab initio methods by utilizing a self-adaptive evolutionary algorithm using Monte Carlo based fragment insertion …”
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Scalable evolutionary clustering algorithm with Self Adaptive Genetic Operators von León, Elizabeth, Nasraoui, Olfa, Gómez, Jonatan

“… In this paper, we present a scalable evolutionary algorithm for clustering large and dynamic data sets, called Scalable Evolutionary Clustering with Self Adaptive Genetic Operators (Scalable ECSAGO …”
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An Automatic Fuzzy Clustering Algorithm Based on Self-adaptive Differential Evolution for Remote Sensing Image

“… This paper proposes an automatic fuzzy clustering method based on self-adaptive differential evolution for remote sensing image (AFCDE …”
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Adaptive Peak Environmental Density Clustering Algorithm in Cloud Computing Technology von Zhao, Nannan

“… — In order to improve the clustering ability of grid sparse unbalanced cloud data sets, an adaptive environment density peak clustering algorithm based on cloud computing technology is proposed …”
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A self-adaptive spectral clustering algorithm von Cai, Xiaoyan, Dai, Guanzhong, Yang, Libin, Zhang, Guoqing

“… As a result, a self-adaptive spectral clustering algorithm based on affinity matrix is proposed …”
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Load Data Analysis Based on Timestamp-Based Self-Adaptive Evolutionary Clustering von Lin, Rongheng, He, Zheyu, Zou, Hua, Wu, Budan

“… When conducting a continuous clustering task for consecutive days, traditional clustering algorithms cannot consider the time-dimension features into analysis, which may make clustering results …”
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