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  1. 1
    Data clustering using the bees algorithm

    Data clustering using the bees algorithm by Pham, D.T, Otri, S., Afify, A., Mahmuddin, Massudi, Al-Jabbouli, H.

    Published 2007
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  2. 2
    A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds

    A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds by Salim, Naomie, Shah, J. Z.

    Published 2007
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  3. 3
    Efficient and scalable ant colony optimization based WSN routing protocol for IoT

    Efficient and scalable ant colony optimization based WSN routing protocol for IoT by Sharmin, Afsah, Anwar, Farhat, Motakabber, S. M. A.

    Published 2020
    “…For this reason, many intelligent systems have been utilized to design routing algorithms to handle the network's dynamic state. In this paper, an ant colony optimization (ACO) based WSN routing algorithm for IoT has been proposed and analyzed to enhance scalability, to accommodate node mobility and to minimize initialization delay for time critical applications in the context of IoT to find the optimal path of data transmission, improvising efficient IoT communications. …”
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  4. 4
    Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours

    Enhancement Of Clustering Algorithm Using 3D Euclidean Distance To Improve Network Connectivity In Wireless Sensor Networks For Correlated Node Behaviours by Kalid Abdlkader Marsal Salih

    Published 2024
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  5. 5
    Modified ACS centroid memory for data clustering

    Modified ACS centroid memory for data clustering by Jabbar, Ayad Mohammed, Ku-Mahamud, Ku Ruhana, Sagban, Rafid

    Published 2019
    “…A comparison of the performance of several common clustering algorithms using real-world data sets shows that the accuracy of the proposed algorithm surpasses those of its counterparts.…”
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  6. 6
    An improved ACS algorithm for data clustering

    An improved ACS algorithm for data clustering by Mohammed Jabbar, Ayad, Ku-Mahamud, Ku Ruhana, Sagban, Rafid

    Published 2020
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  7. 7
    A partition based feature selection approach for mixed data clustering / Ashish Dutt

    A partition based feature selection approach for mixed data clustering / Ashish Dutt by Ashish , Dutt

    Published 2020
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  8. 8
    Initialization Methods For Conventional Fuzzy C-Means And Its Application Towards Colour Image Segmentation

    Initialization Methods For Conventional Fuzzy C-Means And Its Application Towards Colour Image Segmentation by Tan , Khang Siang

    Published 2011
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  9. 9
    Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster

    Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Tutut, Herawan, K., F.Rabbi

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  10. 10
    Clustering chemical data set using particle swarm optimization based algorithm

    Clustering chemical data set using particle swarm optimization based algorithm by Triyono, Triyono

    Published 2008
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  11. 11
    MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data

    MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data by Hongwu, Qin, Ma, Xiuqin, Herawan, Tutut, Jasni, Mohamad Zain

    Published 2014
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  12. 12
    Integrated bisect K-means and firefly algorithm for hierarchical text clustering

    Integrated bisect K-means and firefly algorithm for hierarchical text clustering by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

    Published 2016
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  13. 13
    Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering

    Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering by Hengda, Wang, Mohamad Mohsin, Mohamad Farhan, Mohd Pozi, Muhammad Syafiq

    Published 2024
    “…The fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). …”
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  14. 14
    Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping

    Hybrid Henry gas solubility optimization algorithm with dynamic cluster-to-algorithm mapping by Kamal Z., Zamli, Kader, Md. Abdul, Azad, Saiful, Ahmed, Bestoun S.

    Published 2021
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  15. 15
    Clustering of Chemical Compounds using Unsupervised Neural Networks Algorithms: a comparison

    Clustering of Chemical Compounds using Unsupervised Neural Networks Algorithms: a comparison by Zeb Shah, Jehan, Salim, Naomie

    Published 2006
    “…In this work the applications of a number of self-organizing map (SOM) neural network algorithms to the clustering of chemical datasets are investigated. …”
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  16. 16
    Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison

    Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison by Zeb Shah, Jehan, Salim, Naomie

    Published 2006
    “…In this work the applications of a number of self-organizing map (SOM) neural network algorithms to the clustering of chemical datasets are investigated. …”
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  17. 17
    A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques

    A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques by Sharifah Sakinah, Syed Ahmad

    Published 2014
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  18. 18
    A novel clustering based genetic algorithm for route optimization

    A novel clustering based genetic algorithm for route optimization by Aibinu, Abiodun Musa, Salau, Habeeb Bello, Najeeb, Athaur Rahman, Nwohu, Mark Ndubuka, Akachukwu, Chichebe

    Published 2016
    “…Furthermore, the proposed algorithm converged to global solution within few iterations (generations) thus favoring its acceptability for online-realtime applications. …”
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  19. 19
    Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim

    Parallel genetic algorithms for shortest path routing in high- performance computing / Mohd Erman Safawie Che Ibrahim by Che Ibrahim, Mohd Erman Safawie

    Published 2012
    “…This project has the capability to reduce the execution time of application problem using parallel algorithms to increase efficiency of cluster computing. …”
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  20. 20
    A novel clustering algorithm for mobile ad hoc networks based on determination of virtual links’ weight to increase network stability

    A novel clustering algorithm for mobile ad hoc networks based on determination of virtual links’ weight to increase network stability by Karimi, Abbas, Afsharfarnia, Abbas, Zarafshan, Faraneh, Syed Mohamed, Syed Abdul Rahman Al-Haddad

    Published 2014
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