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  1. 1
    Efficient genetic partitioning-around-medoid algorithm for clustering

    Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

    Published 2019
    “…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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  2. 2
    Clustering ensemble learning method based on incremental genetic algorithms

    Clustering ensemble learning method based on incremental genetic algorithms by Ghaemi, Reza

    Published 2012
    “…So far, limited genetic-based clustering ensemble algorithms have been developed. …”
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  3. 3
    Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection

    Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection by Sinarwati, Mohamad Suhaili, Mohamad Nazim, Jambli

    Published 2011
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  4. 4
    An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis

    An observation of different clustering algorithms and clustering evaluation criteria for a feature selection based on linear discriminant analysis by Tie, K. H., A., Senawi, Chuan, Z. L.

    Published 2022
    “…The higher the TERR threshold value is set, the more the feature subset size will be, regardless of the type of clustering algorithm and the clustering evaluation criterion are used. …”
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  5. 5
    Evaluation of Visual Network Algorithms on Historical Documents

    Evaluation of Visual Network Algorithms on Historical Documents by Khairunnisa, Binti Ibrahim

    Published 2020
    “…The framework suggests to evaluate both graph layout and clustering algorithm in order to produce a good network. …”
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  6. 6
    Centre-based hard clustering algorithms for Y-STR data / Ali Seman, Zainab Abu Bakar and Azizian Mohd. Sapawi

    Centre-based hard clustering algorithms for Y-STR data / Ali Seman, Zainab Abu Bakar and Azizian Mohd. Sapawi by Seman, Ali, Abu Bakar, Zainab, Mohd. Sapawi, Azizian

    Published 2010
    “…This paper presents Centre-based hard clustering approaches for clustering Y-STR data. …”
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  7. 7
    Adaptive grid-meshed-buffer clustering algorithm for outlier detection in evolving data stream

    Adaptive grid-meshed-buffer clustering algorithm for outlier detection in evolving data stream by Abdulateef, Alaa Fareed

    Published 2023
    “…Evaluation was made based on various evaluation metrics related to outlier detection and clustering quality. …”
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  8. 8
    On density-based data streams clustering algorithms: A survey

    On density-based data streams clustering algorithms: A survey by Teh, Y.W.

    Published 2017
    “…Recently, a lot of density-based clustering algorithms are extended for data streams. …”
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  9. 9
    An online density-based clustering algorithm for data stream based on local optimal radius and cluster pruning

    An online density-based clustering algorithm for data stream based on local optimal radius and cluster pruning by Islam, Md Kamrul

    Published 2019
    “…In this study, a fully online density-based clustering algorithm called Buffer-based Online Clustering for Evolving Data Stream (BOCEDS) is presented. …”
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  10. 10
    Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network

    Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network by Vincent Chung, Norah Tuah, Kit Guan Lim, Min Keng Tan, Ismail Saad, Kenneth Tze Kin Teo

    Published 2020
    “…Based on the performance evaluation, GACS outperforms both Genetic Algorithm (GA)-based cluster optimization algorithm and Cuckoo Search (CS)-based multi-hop optimization algorithm.…”
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  11. 11
    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms

    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

    Published 2018
    “…Nature-inspired optimization-based clustering techniques are powerful, robust and more sophisticated than the conventional clustering methods due to their stochastic and heuristic characteristics. …”
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  12. 12
    MuDi-Stream: A multi density clustering algorithm for evolving data stream

    MuDi-Stream: A multi density clustering algorithm for evolving data stream by Amini, A., Saboohi, H., Herawan, T., Teh, Y.W.

    Published 2016
    “…The offline phase generates the final clusters using an adapted density-based clustering algorithm. …”
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  13. 13
    Image segmentation based on normalised cuts with clustering algorithm

    Image segmentation based on normalised cuts with clustering algorithm by Choong, Mei Yeen

    Published 2013
    “…Evaluation of c -means and fuzzy c-means clustering algorithm with normalised cuts image segmentation on various kinds of images has been carried out. …”
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  14. 14
    Document clustering based on firefly algorithm

    Document clustering based on firefly algorithm by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

    Published 2015
    “…Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters.Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization.This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering.We present two variants of FA; Weight- based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFAII).The difference between the two algorithms is that the WFAII, includes a more restricted condition in determining members of a cluster.The proposed FA methods are later evaluated using the 20Newsgroups dataset.Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. …”
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  15. 15
    Biological-based semi-supervised clustering algorithm to improve gene function prediction

    Biological-based semi-supervised clustering algorithm to improve gene function prediction by Kasim, Shahreen, Deris, Safaai, M. Othman, Razib, Hashim, Rathiah

    Published 2011
    “…We tested our new computational framework for semi-supervised clustering of yeast gene expression data based onmultiple functional annotation databases. …”
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  16. 16
    Transitive fuzzy similarity multigraph-based model for alternative clustering in multi-criteria group decision making problems

    Transitive fuzzy similarity multigraph-based model for alternative clustering in multi-criteria group decision making problems by Khameneh, Azadeh Zahedi, Kilicman, Adem, Md Ali, Fadzilah

    Published 2022
    “…The present paper contributes to considering a novel data clustering algorithm, called FBC-Cluster, based on fuzzy multigraphs in terms of both structural and attribute similarities. …”
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  17. 17
    CC_TRS: continuous clustering of trajectory stream data based on micro cluster life

    CC_TRS: continuous clustering of trajectory stream data based on micro cluster life by Abdulrazzaq, Musaab Riyadh, Mustapha, Norwati, Sulaiman, Md. Nasir, Mohd Sharef, Nurfadhlina

    Published 2017
    “…In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. …”
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  18. 18
    Fuzzy clustering method and evaluation based on multi criteria decision making technique

    Fuzzy clustering method and evaluation based on multi criteria decision making technique by Sameer, Fadhaa Othman

    Published 2018
    “…This proposed algorithm is developed based on heuristic method named modified binary particle swarm optimization (MBPSO) with kernel fuzzy clustering method as a fitness function. …”
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  19. 19
    An adaptive density-based method for clustering evolving data streams / Amineh Amini

    An adaptive density-based method for clustering evolving data streams / Amineh Amini by Amini, Amineh

    Published 2014
    “…This study proposes a density-based algorithm for clustering evolving data streams. …”
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  20. 20
    A hybrid Method Of Centroid-Based Clustering And Meta-Heuristic For Personalized Web Search

    A hybrid Method Of Centroid-Based Clustering And Meta-Heuristic For Personalized Web Search by Bourair Sadik Mohamad Taqi

    Published 2024
    thesis::doctoral thesis
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