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  1. 1
    A new variant of black hole algorithm based on multi population and levy flight for clustering problem

    A new variant of black hole algorithm based on multi population and levy flight for clustering problem by Haneen Abdul Wahab, Abdul Raheem

    Published 2020
    “…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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  2. 2
    Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman

    Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman by Seman, Ali

    Published 2013
    “…This uniqueness of Y-STR data has become problematic in partitioning the data using the existing partitional clustering algorithms. …”
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  3. 3
    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
    “…However, existing density-based data stream clustering algorithms are not without problems. …”
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  4. 4
    An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets

    An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets by Azlin, Ahmad, Rubiyah, Yusof, Nor Saradatul Akmar, Zulkifli, Mohd Najib, Ismail

    Published 2021
    “…The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. …”
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  5. 5
    An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets

    An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets by Ahmad, Azlin, Yusof, Rubiyah, Zulkifli, Nor Saradatul Akma, Ismail, Mohd Najib

    Published 2021
    “…The data distribution issue remains an unsolved clustering problem in data mining, especially in dealing with imbalanced datasets. …”
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  6. 6
    An improved self organizing map using jaccard new measure for textual bugs data clustering

    An improved self organizing map using jaccard new measure for textual bugs data clustering by Ahmed, Attika

    Published 2018
    “…Considering the unsupervised learning algorithms, Self-Organizing Map (SOM) considers the equally compatible algorithm for clustering, as both algorithms are closely related but different in way they were used in data mining. …”
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  7. 7
    An improved self organizing map using jaccard new measure for textual bugs data clustering

    An improved self organizing map using jaccard new measure for textual bugs data clustering by Ahmed, Attika

    Published 2018
    “…Considering the unsupervised learning algorithms, Self­Organizing Map (SOM) considers the equally compatible algorithm for clustering, as both algorithms are closely related but different in way they were used in data mining. …”
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  8. 8
    Application of Optimization Methods for Solving Clustering and Classification Problems

    Application of Optimization Methods for Solving Clustering and Classification Problems by Shabanzadeh, Parvaneh

    Published 2011
    “…The data vectors are assigned to the closest cluster and correspondingly to the set, which contains this cluster and an algorithm based on a derivative-free method is applied to the solution of this problem. …”
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  9. 9
    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
    “…For the third problem a modified of Kohonen Network (MKN) algorithm was proposed to select the initial centres of clusters. …”
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  10. 10
    An enhanced version of black hole algorithm via levy flight for optimization and data clustering problems

    An enhanced version of black hole algorithm via levy flight for optimization and data clustering problems by Abdulwahab, Haneen A., Noraziah, Ahmad, Al-Sewari, Abdul Rahman Ahmed Mohammed, Salih, Sinan Q.

    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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  11. 11
    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
    “…There are a few clustering algorithms for handling mixed data. Clustering mixed data is dependent on the dissimilarities of its constituent features. …”
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  12. 12
    An enhanced version of black hole algorithm via levy flight for optimization and data lustering problems

    An enhanced version of black hole algorithm via levy flight for optimization and data lustering problems by Haneen, Abd Wahab, Noraziah, Ahmad, Alsewari, Abdulrahman A., Sinan, Q. Salih

    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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  13. 13
    Improved clustering using robust and classical principal component

    Improved clustering using robust and classical principal component by Hassn, Ahmed Kadom

    Published 2017
    “…k-means algorithm is a popular data clustering algorithm. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. …”
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  14. 14
    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm

    Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm by Dalatu, Paul Inuwa

    Published 2018
    “…The K-Means algorithm is the commonest and fast technique in partitional cluster algorithms, although with unnormalized datasets it can achieve local optimal. …”
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  15. 15
    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

    Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model by Sulaiman, Md. Nasir, Mohamed, Raihani, Mustapha, Norwati, Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…Expectation maximization (EM) is one of the representatives clustering algorithms which have broadly applied in solving classification problems by improving the density of data using the probability density function. …”
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  16. 16
    Automatic clustering of gene ontology by genetic algorithm

    Automatic clustering of gene ontology by genetic algorithm by Othman, Razib M., Deris, Safaai, Zakaria, Zalmiyah, Illias, Rosli M., Mohamad, Saberi M.

    Published 2006
    “…Additionally, deciding the number k of clusters to use is not easily perceived and is a hard algorithmic problem. …”
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  17. 17
    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
    “…Clustering is concerned with partitioning a data set into homogeneous groups. …”
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  18. 18
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). Phase 2 is to study the performance of proposed integration system which using the data clustered to be used as train data for Naïve Bayes classifier. …”
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  19. 19
    Optimized clustering with modified K-means algorithm

    Optimized clustering with modified K-means algorithm by Alibuhtto, Mohamed Cassim

    Published 2021
    “…Generally, the proposed modified k-means algorithm is able to determine the optimum number of clusters for huge data.…”
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  20. 20
    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
    “…This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. …”
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