Search Results - (( process evaluation clustering algorithm ) OR ( java application optimisation algorithm ))
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1
Adaptive grid-meshed-buffer clustering algorithm for outlier detection in evolving data stream
Published 2023“…Evaluation was made based on various evaluation metrics related to outlier detection and clustering quality. …”
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2
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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3
An online density-based clustering algorithm for data stream based on local optimal radius and cluster pruning
Published 2019“…These results prove the superiority of BOCEDS algorithm over the existing clustering algorithms. …”
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4
Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…The latter drawbacks are consequences of the difficulty in balancing the exploration and exploitation processes which directly affect the final quality of the clustering solutions. …”
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5
Fuzzy clustering method and evaluation based on multi criteria decision making technique
Published 2018“…The proposed algorithm is used as a pre-processing method for data followed by Gustafson-Kessel (GK) algorithm to classify credit scoring data. …”
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6
Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network
Published 2023“…The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. …”
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Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim
Published 2012“…The objectives of this project to set-up Beowulf cluster computer to apply the Travelling Salesman Problem in parallel by using Genetic Algorithms and evaluate sequential algorithms and parallel algorithms by Genetic Algorithms. …”
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8
The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data
Published 2024“…In the partitioning phase, the data is partitioned into smaller subsets that can be clustered in parallel across multiple nodes. The suggested method, implemented in the Databricks cloud platform provides high clustering accuracy, as measured by clustering evaluation metrics such as the silhouette coefficient, cost function, partition index, clustering entropy. …”
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Integrated bisect K-means and firefly algorithm for hierarchical text clustering
Published 2016“…At each level of the proposed Bisect FA, firefly algorithm works to produce the best clusters. For evaluation purposes, we performed experiments on 20 newsgroups dataset that is commonly used in text clustering studies.The results demonstrate that Bisect FA obtains more accurate and compact clustering than Bisect K-means, K-means and C-firefly algorithms. …”
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On density-based data streams clustering algorithms: A survey
Published 2017“…Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms’ performance. …”
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11
A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds
Published 2007“…In this work a fuzzy hierarchical algorithm is developed which provides a mechanism not only to benefit from the fuzzy clustering process but also to get advantage of the multiple membership function of the fuzzy clustering. …”
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Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
Published 2024“…The findings of the experiments are compared to the outcomes of BOCEDS, CEDAS, and MuDi-Stream algorithms. The outcomes indicate that the EWR algorithm outperformed the baseline clustering algorithms. …”
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Biological-based semi-supervised clustering algorithm to improve gene function prediction
Published 2011“…However, commonclustering algorithms do not provide a comprehensive approach that look into the three categories of annotations; biologicalprocess, molecular function, and cellular component, and were not tested with different functional annotation database formats.Furthermore, the traditional clustering algorithms use random initialization which causes inconsistent cluster generation and areunable to determine the number of clusters involved. …”
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Clustering of Chemical Compounds using Unsupervised Neural Networks Algorithms: a comparison
Published 2006“…Clustering of chemical databases has tremendous significance in the process of compound selection, virtual screening and in the drug designing and discovery process as a whole. …”
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15
Segmentation of MRI brain images using statistical approaches
Published 2011“…Moreover, three improvements of EM for brain MRI segmentation are proposed, which incorporate neighbourhood information in a new manner in the clustering process. In addition, two algorithms for the post-processing of clustering results using user-interaction and the re-evaluation of boundary data in each cluster are presented. …”
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16
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). …”
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17
Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison
Published 2006“…Clustering of chemical databases has tremendous significance in the process of compound selection, virtual screening and in the drug designing and discovery process as a whole. …”
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18
An Efficient Cluster Head Election Algorithm for Client Mesh Networks using Fuzzy Logic Control
Published 2017“…Simulation experiments were conducted to evaluate the performance of FLCCA in terms of the number of clusters formed, reaffiliation count and clustering control overheads. …”
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Computational Discovery of Motifs Using Hierarchical Clustering Techniques
Published 2008“…A mismatch based hierarchical clustering algorithm is proposed in this paper, where three heuristic rules for classifying clusters and a post-processing for ranking and refining the clusters are employed in the algorithm. …”
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Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…The clustering algorithm consists of two components: the temporal micro-clusters generation and the temporal micro clusters merging. …”
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