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  1. 1
    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform

    Mathematical simulation for 3-dimensional temperature visualization on open source-based grid computing platform by Alias, Norma, Satam, Noriza, Abd. Ghaffar, Zarith Safiza, Darwis, Roziha, Hamzah, Norhafiza, Islam, Md. Rajibul

    Published 2009
    “…New Iterative Alternating Group Explicit (NAGE) is a powerful parallel numerical algorithm for multidimensional temperature prediction. …”
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  2. 2
    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms

    Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms by Teoh, Chin Chuang

    Published 2005
    “…In cluster labelling process, a cluster labelling algorithm based on calculation of minimum-distance (MD) between cluster mean and class mean was developed to label the clusters. …”
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    Thesis
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  3. 3
    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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  4. 4
    Fuzzy Soft Set Clustering for Categorical Data

    Fuzzy Soft Set Clustering for Categorical Data by Iwan Tri Riyadi, Yanto, Ani, Apriani, Rofiul, Wahyudi, Cheah, Wai Shiang, Suprihatin, in, Rahmat, Hidayat

    Published 2024
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  5. 5
    Clustering autism spectrum disorder student’s system based on intelligence, skills and behavior using agglomerative clustering algortihm / Daarin Nadia Nordin

    Clustering autism spectrum disorder student’s system based on intelligence, skills and behavior using agglomerative clustering algortihm / Daarin Nadia Nordin by Nordin, Daarin Nadia

    Published 2020
    “…Data cleaning and data transformation is first carried out, followed by normalization through the Z-score method before being processed in the clustering model. Several charts such as dendrograms and sunburst charts are used to visualize the clustering results. …”
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  6. 6
    Customer mobile behavioral segmentation and analysis in telecom using machine learning

    Customer mobile behavioral segmentation and analysis in telecom using machine learning by Sharaf Addin, Eman Hussein, Admodisastro, Novia Indriaty, Mohd Ashri, Siti Nur Syahirah, Kamaruddin, Azrina, Chew, Yew Chong

    Published 2021
    “…Lastly, a dashboard was developed using Microsoft Power BI to visualize the clustering results in meaningful analysis. …”
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  7. 7
    Visualization of dengue incidences using expectation maximization (EM) algorithm

    Visualization of dengue incidences using expectation maximization (EM) algorithm by Mathur, N., Asirvadam, V.S., Dass, S.C., Gill, B.S.

    Published 2017
    “…R-GIS(R software) and clustering algorithm were used for year 2014 with several weeks to develop the relation between the visualization and prediction of reported incidences. …”
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  8. 8
    Agarwood oil grade clustering of aquilaria malaccensis species using Extraction by GC-MS analysis: Efficient KNN algorithm based on patterns visualization of two-dimensional graph

    Agarwood oil grade clustering of aquilaria malaccensis species using Extraction by GC-MS analysis: Efficient KNN algorithm based on patterns visualization of two-dimensional graph by Siti Mariatul Hazwa, Mohd Huzir, Anis Hazirah ‘Izzati H., Al-Hadi, Amir Hussairi, Zaidi, Nurlaila F., Ismail, Zakiah, Mohd Yusoff, Saiful Nizam, Tajuddin, Mohd Nasir, Taib

    Published 2024
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  9. 9
    Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning

    Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning by Lim, Wai Ping, Goh, Ching Pang

    Published 2023
    “…The findings underscore the pivotal role of AI in navigating and unraveling customer behavior complexities in the banking industry. All clustering models are developed successfully in producing cluster results. …”
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  10. 10
    K-means Clustering Analysis for EEG Features of Situational Interest Detection in Classroom Learning

    K-means Clustering Analysis for EEG Features of Situational Interest Detection in Classroom Learning by Othman, E.S., Faye, I., Babiker, A., Hussaan, A.M.

    Published 2021
    “…The mean power for each sub-frequency band was served as input to the k-means algorithm. Results showed that EEG features can be successfully clustered in the alpha frequency band at the frontal region when visual-auditory stimuli are introduced to the subjects. …”
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  11. 11
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  12. 12
    Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

    Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI by Abang Mohd Arif Anaqi, Abang Isa, Kuryati, Kipli, Ahmad Tirmizi, Jobli, Muhammad Hamdi, Mahmood, Siti Kudnie, Sahari, Aditya Tri, Hernowo, Sinin, Hamdan

    Published 2021
    “…The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. …”
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  13. 13
    Application of genetic algorithm and JFugue in an evolutionary music generator

    Application of genetic algorithm and JFugue in an evolutionary music generator by Tang, Jia Rou

    Published 2025
    “…This project explores the application of Genetic Algorithms (GA) with JFugue, which is a Java-based music programming library to develop an Evolutionary Music Generator. …”
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  14. 14
    3D surface roughness measurement for scaliness scoring of psoriasis lesions

    3D surface roughness measurement for scaliness scoring of psoriasis lesions by Ahmad Fadzil, M.H., Prakasa, E., Asirvadam, V.S., Nugroho, H., Affandi, A.M., Hussein, S.H.

    Published 2013
    “…The reliability of the developed PASI scaliness algorithm was high with kappa coefficients>0.84 (almost perfect agreement). …”
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  15. 15
    Unsupervised place recognition for assistive mobile robots based on local feature descriptions.

    Unsupervised place recognition for assistive mobile robots based on local feature descriptions. by Tang, Sai Hong, Ramli, Abdul Rahman, Samsudin, Khairulmizam

    Published 2011
    “…In this paper, a robust appearance-based unsupervised place clustering and recognition algorithm is introduced. …”
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  16. 16
    The development of level of detail (LOD) technique in 3D computer graphics application

    The development of level of detail (LOD) technique in 3D computer graphics application by Ismail, Nor Anita Fairos, Daman, Daut, Mohd. Rahim, Mohd. Shafry

    Published 2009
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    Monograph
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  17. 17
    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning

    Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

    Published 2016
    “…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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  18. 18
    Unsupervised and online place recognition for mobile robot based on local features description

    Unsupervised and online place recognition for mobile robot based on local features description by Tang, Sai Hong, Hamami, Ghazali, Karasfi, Babak, Nakhaeinia, Danial

    Published 2013
    “…In this paper, a robust appearance-based unsupervised and online place recognition algorithm, which is inspired from online sequential clustering methods, is introduced. …”
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  19. 19
    Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques

    Unsupervised classification of multi-class chart images: A comparison of customized CNNs and transfer learning techniques by Hassan Zaidi, Syed Muhammad, Jamil Alsayaydeh, Jamil Abedalrahim, Khan, Abdul Hafeez, Khan, Abdullah Ayub, AlZubi, Ahmad Ali, Ogunshola, Benny, Herawan, Safarudin Gazali

    Published 2025
    “…The k-means clustering algorithm is then applied to group visually similar chart images. …”
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  20. 20
    Quantitative Analysis And Mapping Of Concrete Scanning Electron Microscope (SEM) Images

    Quantitative Analysis And Mapping Of Concrete Scanning Electron Microscope (SEM) Images by Maizul, Elly Nur Myaisara

    Published 2018
    “…By spatial segmentation of K-Means algorithms, the cluster groups generated were carefully reviewed before proceeding to the final analysis. …”
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