Search Results - (( develop computing clustering algorithm ) OR ( java implication _ algorithm ))
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MuDi-Stream: A multi density clustering algorithm for evolving data stream
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Computational Discovery of Motifs Using Hierarchical Clustering Techniques
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Proceeding -
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Data clustering using the bees algorithm
Published 2007“…One of the most popular clustering methods is k-means clustering because of its simplicity and computational efficiency. …”
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Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
Published 2024“…Lately, a fully online buffer-based clustering algorithm for handling evolving data streams (BOCEDS) was developed. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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Thesis -
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Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization
Published 2024thesis::doctoral thesis -
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Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
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Citation Index Journal -
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Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
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Citation Index Journal -
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An adaptive density-based method for clustering evolving data streams / Amineh Amini
Published 2014“…Due to these characteristics the traditional densitybased clustering is not applicable. Recently, a number of density-based algorithms have been developed for clustering data streams. …”
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Gas Identi cation by Using a Cluster-k-Nearest-Neighbor
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Optimized clustering with modified K-means algorithm
Published 2021“…The proposed algorithm utilised a distance measure to compute the between groups’ separation to accelerate the process of identifying an optimal number of clusters using k-means. …”
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Thesis -
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The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data
Published 2024“…Therefore, we develop a Parallel Fuzzy C-Median Clustering Algorithm Using Spark for Big Data that can handle large datasets while maintaining high accuracy and scalability. …”
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Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning
Published 2019“…Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. …”
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Parallelization of noise reduction algorithm for seismic data on a beowulf cluster
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