Search Results - (( data distribution clustering algorithm ) OR ( data optimization method algorithm ))
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An enhanced version of black hole algorithm via levy flight for optimization and data lustering problems
Published 2019“…Recently, nature-inspired algorithms have been proposed and utilized for solving the optimization problems in general, and data clustering problem in particular. …”
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An enhanced version of black hole algorithm via levy flight for optimization and data clustering problems
Published 2019“…Recently, nature-inspired algorithms have been proposed and utilized for solving the optimization problems in general, and data clustering problem in particular. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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Optimizing wireless sensor networks: A survey of clustering strategies and algorithms
Published 2024“…Clustering helps in the distribution of energy evenly in the network minimizing the number of unnecessary transmissions. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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Investigation on the dynamic of computation of semi autonomous evolutionary computation for syntactic optimization of a set of programming codes
Published 2007“…In order to have a realistic characteristic of a parallel computing engine, a Rocks based computer cluster was built and used for the test. Genetic Algorithm as one of the Evolutionary Computation method improve the execution of parallel programming codes by optimizing the number of processors and the distribution of data. …”
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Enhancing high-dimensional streaming data analysis: optimizing Online Feature Selection for handling drift using optimization technique and ensemble learning
Published 2024“…This research addresses the challenge by devel-oping innovative solutions in optimizing Online Feature Selection (OFS) to manage feature irrelevancy and redundancy, tackling the issues of Feature Drift, and rigor-ously validating the proposed algorithms in high-dimensional dynamic data streams. …”
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Improved K-means clustering and adaptive distance threshold for energy reduction in WSN-IoTs
Published 2025“…This study introduces an enhanced energy aware clustering approach that combines an improved K-Means algorithm with an adaptive distance threshold to optimize relay node selection and cluster formation. …”
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Enhancing clustering algorithm with initial centroids in tool wear region recognition
Published 2020“…The silhouette value average score is 0.8504 (highest score is 0.9207) of how well-distributed the resulting clusters. The clustering system has identified the tool to stop cutting at approximate VB = 0.213 mm before the tool condition enters the failure region of abnormal phase (VB < 0.250 mm).s The proposed system functioned effectively in clustering the data obtained from cutting tests performed within a reasonable range of wear stages. …”
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4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer
Published 2023“…Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. …”
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Scheduled activity energy-aware distributed cluster- based routing algorithm for wireless sensor networks with non-uniform node distribution
Published 2014“…Therefore, in this study, a new algorithm called Scheduled-Activity Energy Aware Distributed Clustering (SA-EADC) is proposed. …”
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Fuzzy Soft Set Clustering for Categorical Data
Published 2024“…This research provides categorical data with fuzzy clustering technique due to soft set theory and multinomial distribution. …”
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Statistical performance of agglomerative hierarchical clustering technique via pairing of correlation-based distances and linkage methods
Published 2025“…Five tables of summary for choosing appropriate clustering algorithms according to data distribution were produced. …”
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A scheduled activity energy aware distributed clustering algorithm for wireless sensor networks with nonuniform node distribution
Published 2014“…Energy aware distributed clustering (EADC) is one of the cluster-based routing protocols proposed for networks with nonuniform node distribution, which can effectively balance the energy consumption among the nodes. …”
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Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm
Published 2025“…Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout's distributed machine-learning environment. …”
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A cluster-based hybrid replica control protocol for high availability in data grid
Published 2019“…In Data Grid, data replication is a widely used technique for managing data, where exact copies of data or replicas are created and stored at many distributed sites. …”
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An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets
Published 2021“…The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. …”
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An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets
Published 2021“…The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. …”
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An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis
Published 2022“…The k-means and the Gaussian mixture distribution were adopted as the clustering algorithms and each algorithm was tested on four datasets with four distinct clustering evaluation criteria: Calinski-Harabasz, Davies-Bouldin, Gap and Silhouette. …”
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