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  1. 1
    Reliability fuzzy clustering algorithm for wellness of elderly people

    Reliability fuzzy clustering algorithm for wellness of elderly people by N. J., Mohd Jamal, Ku Muhammad Naim, Ku Khalif, Mohd Sham, Mohamad

    Published 2019
    “…Thus, the objective of this paper is to propose a reliable fuzzy clustering algorithm using z-numbers. This model will demonstrate the capability to handle the knowledge of human being and uncertain information in evaluating the wellness of chronic kidney disease (CKD) patients. …”
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  2. 2
    Efficient genetic partitioning-around-medoid algorithm for clustering

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

    Published 2019
    “…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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  3. 3
    Evaluation on rapid profiling with clustering algorithms for plantation stocks on Bursa Malaysia

    Evaluation on rapid profiling with clustering algorithms for plantation stocks on Bursa Malaysia by Keng, Hoong Ng, Kok, Chin Khor

    Published 2016
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  4. 4
    On density-based data streams clustering algorithms: A survey

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

    Published 2017
    “…Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms’ performance. …”
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  5. 5
    Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization

    Algorithm Development of Bidirectional Agglomerative Hierarchical Clustering Using AVL Tree with Visualization by Hussain Mohammad Yousef Abu Dalbouh

    Published 2024
    thesis::doctoral thesis
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  6. 6
    Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction

    Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction by Alkawsi G., Al-amri R., Baashar Y., Ghorashi S., Alabdulkreem E., Kiong Tiong S.

    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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  7. 7
    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
    “…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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  8. 8
    A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

    A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data by Shirkhorshidi, A.S., Aghabozorgi, S., Wah, T.Y.

    Published 2015
    “…Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. …”
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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
    “…These results prove the superiority of BOCEDS algorithm over the existing clustering algorithms. …”
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  10. 10
    Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering

    Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering by Rahmah, Mokhtar, Raza, Muhammad Ahsan, Fauziah, Zainuddin, Nor Azhar, Ahmad, Raza, Muhammad Fahad, Raza, Binish

    Published 2021
    “…Our methodology incorporated ontology to filter the datasets and exploited Rapidminer environment to evaluate the performance of clustering algorithms. …”
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  11. 11
    A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube

    A Clustering Algorithm for Evolving Data Streams Using Temporal Spatial Hyper Cube by Al?amri R., Murugesan R.K., Almutairi M., Munir K., Alkawsi G., Baashar Y.

    Published 2023
    “…Evaluation based on both the real world and synthetic datasets has proven the superiority of the developed BOCEDS TSHC clustering algorithm over the baseline algorithms with respect to most of the clustering met-rics. � 2022 by the authors. …”
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  12. 12
    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
    “…Density-based method has emerged as a worthwhile class for clustering data streams. It has the abilities to discover clusters of arbitrary shapes, handle noise, and cluster without prior knowledge of number of clusters. …”
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  13. 13
    The Impact of Exponent Variable on the Performance and Effectiveness of FCM Algorithm for Ontology Construction in Structured Knowledge Management

    The Impact of Exponent Variable on the Performance and Effectiveness of FCM Algorithm for Ontology Construction in Structured Knowledge Management by Mahmood, Khalid, Rahmah, Mokhtar, Fauziah, Zainuddin, Nor Azhar, Ahmad, Noraziah, Ahmad, Norshita, MN

    Published 2021
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  14. 14
    Datasets Size: Effect on Clustering Results

    Datasets Size: Effect on Clustering Results by Raheem, Ajiboye Adeleke, Ruzaini, Abdullah Arshah, Hongwu, Qin

    Published 2013
    “…The clustering results were validated using external evaluation measure in order to determine their level of correctness. …”
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  15. 15
    Centre based evolving clustering framework with extended mobility features for vehicular ad-hoc networks

    Centre based evolving clustering framework with extended mobility features for vehicular ad-hoc networks by Talib, Mohammed Saad

    Published 2021
    “…This framework uses an evolving data clustering algorithm by adopting the concept of grid granularity to capture the features of a cluster more efficiently. …”
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  16. 16
    A spark-based parallel fuzzy C median algorithm for web log big data

    A spark-based parallel fuzzy C median algorithm for web log big data by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Nizamuddin, Mohammed Khaja, Sarkar, Rashal, Chalil, Aboosalih Kakkat

    Published 2022
    “…Based on the Rand Index and SSE (sum of squared error), the parallel Fuzzy C median algorithm's performance is evaluated in the PySpark platform. …”
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  17. 17
    Evaluation of Clustering and Multi-hop Routing Protocols in Mobile Ad-hoc Sensor Networks

    Evaluation of Clustering and Multi-hop Routing Protocols in Mobile Ad-hoc Sensor Networks by Jambli, M.N., Hendrick, A., Julaihi, A.A., Abdullah, J., Suhaili, S.M.

    Published 2015
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    Proceeding
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  18. 18
    Out-of-core simplification with appearance preservation for computer game applications

    Out-of-core simplification with appearance preservation for computer game applications by Tan, Kim Heok

    Published 2006
    “…Unlike any other vertex clustering methods, the knowledge of neighbourhood between nodes is unnecessary and the node simplification is performed independently. …”
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  19. 19
    KM-NEU: an efficient hybrid approach for intrusion detection system

    KM-NEU: an efficient hybrid approach for intrusion detection system by Lisehroodi, Mazyar Mohammadi, Muda, Zaiton, Yassin, Warusia, Udzir, Nur Izura

    Published 2014
    “…Performance of this hybrid approach is evaluated with standard knowledge discovery in databases (KDD Cup ’99) dataset. …”
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  20. 20
    Out-of-core simplification with appearance preservation for computer game applications

    Out-of-core simplification with appearance preservation for computer game applications by Bade, Abdullah, Daman, Daut, Sunar, Mohd. Shahrizal, Tan , Kim Heok

    Published 2006
    “…Unlike any other vertex clustering methods, the knowledge of neighbourhood between nodes is unnecessary and the node simplification is performed independently. …”
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