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  1. 1

    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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    Thesis
  2. 2

    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

    Published 2012
    “…In soft clustering approach, Kohonen network was employed to find the number of clusters and then the allocation of sites to the appropriate cluster was performed by using fuzzy c-means method. …”
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  3. 3

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

    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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  4. 4

    Clustering Methods For Cluster-Based Routing Protocols In Wireless Sensor Networks: Comparative Study by Hassan, Ali Abdul Hussian, Md Shah, Wahidah, Jabbar Mohammed, Ali Abdul, Othman, Mohd Fairuz Iskandar

    Published 2017
    “…So, the energy-efficient routing protocols are very necessary and considers vital task for sensors networks. Various approaches of clustering algorithms are used to optimize the energy of routing protocols. …”
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  5. 5

    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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  6. 6

    An ensemble data summarization approach based on feature transformation to learning relational data by Chung, Seng Kheau

    Published 2015
    “…A genetic algorithm (GA) is also used to find the best centroids for all the clusters generated cluster centroids. …”
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  7. 7

    Weight-based firefly algorithm for document clustering by Mohammed, Athraa Jasim, Yusof, Yuhanis, Husni, Husniza

    Published 2013
    “…FA is a nature-inspired algorithm that is used in many optimization problems.The FA is realized in document clustering by executing it on Reuters-21578 database.The algorithm identifies documents that has the highest light intensity in a search space and represents it as a centroid.This is followed by recognizing similar documents using the cosine similarity function.Documents that are similar to the centroid are located into one cluster and dissimilar in the other.Experiments performed on the chosen dataset produce high values of Purity and F-measure.Hence, suggesting that the proposed Firefly algorithm is a possible approach in document clustering.…”
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  8. 8

    The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Siddiqui, Sumrana, Sarkar, Rashel

    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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  9. 9

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

    Published 2011
    “…This method does not explicitly use derivatives, and is particularly appropriate when functions are non-smooth. …”
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    Methodology for modified whale optimization algorithm for solving appliances scheduling problem by Omar, Mohd Faizal, Mohd Bakeri, Noorhadila, Mohd Nawi, Mohd Nasrun, Hairani, Norfazlirda, Khalid, Khalizul

    Published 2020
    “…Whale Optimization Algorithm (WOA) is considered as one of the newest metaheuristic algorithms to be used for solving a type of NP-hard problems. …”
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  12. 12

    Metaheuristic Algorithm for Wellbore Trajectory Optimization by Biswas, K., Vasant, P.M., Vintaned, J.A.G., Watada, J.

    Published 2019
    “…Till today so many approaches and methods are used to optimize this wellbore trajectory. …”
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  13. 13

    An Improved Wavelet Neural Network For Classification And Function Approximation by Ong , Pauline

    Published 2011
    “…First, the types of activation functions used in the hidden layer of the WNN were varied. …”
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    Broadening selection competitive constraint handling algorithm for faster convergence by Shaikh, T.A., Hussain, S.S., Tanweer, M.R., Hashmani, M.A.

    Published 2020
    “…The proposed algorithm has been evaluated using 24 benchmark functions. …”
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  17. 17

    Framework for stream clustering of trajectories based on temporal micro clustering technique by Abdulrazzaq, Musaab Riyadh

    Published 2018
    “…The clustering algorithm consists of two components: the temporal micro-clusters generation and the temporal micro clusters merging. …”
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  18. 18

    A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective by Ul haq, Riaz, Norrozila, Sulaiman, Muhammad, Alam

    Published 2013
    “…This survey paper is focused on the discussion of best optimal path routing algorithms in wireless sensor networks by using supervised machine learning approaches. …”
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    An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. by S. Ahmed, Bestoun, Enoiu, Eduard, Afzal, Wasif, Kamal Z., Zamli

    Published 2020
    “…The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyperheuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time.…”
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