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  1. 1
    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer

    Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

    Published 2017
    “…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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  2. 2
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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  3. 3
    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment

    Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment by Shaliza Hayati A. Wahab, Nordin Saad, Azali Saudi, Ali Chekima

    Published 2021
    “…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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  4. 4
    Smart appointment organizer for mobile application / Mohd Syafiq Adam

    Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

    Published 2009
    “…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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  5. 5
    Optimized clustering with modified K-means algorithm

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

    Published 2021
    “…Among the techniques, the k-means algorithm is the most commonly used technique for determining optimal number of clusters (k). …”
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  6. 6
    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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  7. 7
    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

    “…The experiment of the Max-D means has been conducted using synthetic data, which is taken from the Llyod’s K-Means experiments. …”
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  8. 8
    Clustering of rainfall data using k-means algorithm

    Clustering of rainfall data using k-means algorithm by Mohd Sham, Mohamad, Yuhani, Yusof, Ku Muhammad Na’im, Ku Khalif, Mohd Khairul Bazli, Mohd Aziz

    Published 2019
    “…Clustering algorithms in data mining is the method for extracting useful information for a given data. …”
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  9. 9
    Clustering for binary data sets by using genetic algorithm-incremental K-means

    Clustering for binary data sets by using genetic algorithm-incremental K-means by Saharan, S., Baragona, R., Nor, M. E., Salleh, R. M., Asrah, N. M.

    Published 2018
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  10. 10
    MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters

    MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters by Wan Maseri, Wan Mohd, Beg, Abul Hashem, Herawan, Tutut, Fazley Rabbi, Khandakar

    Published 2012
    “…The experiment of the Max-D means has been conducted using synthetic data, which is taken from the Llyod’s K-Means experiments. …”
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  11. 11
    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm

    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

    Published 2025
    “…After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. …”
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  12. 12
    Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics classification profiles using k-means algorithms

    Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics classification profiles using k-means algorithms by Kamarul Ismail, Nasir Nayan, Siti Naielah Ibrahim

    Published 2016
    “…Clustering is one of the important methods in data exploratory in this era because it is widely applied in data mining.Clustering of data is necessary to produce geo-demographic classification where k-means algorithm is used as cluster algorithm. …”
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  13. 13
    Spatial Clustering Algorithm for Time Series Rainfall Data Using X-Means Data Splitting

    Spatial Clustering Algorithm for Time Series Rainfall Data Using X-Means Data Splitting by Ali, Noor Rasidah, Ku Mahamud, Ku Ruhana

    Published 2017
    “…Therefore, a clustering algorithm by introducing data transformation using X-means data splitting is proposed to investigate the spatial homogeneity of time series rainfall data. …”
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  14. 14
    Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment

    Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment by Saeed, Gamil Abdulraqeb Abdullah, Chuan, Zun Liang, Roslinazairimah, Zakaria, Wan Nur Syahidah, Wan Yusoff

    Published 2016
    “…The proposed algorithms use descriptive measures of the data, including arithmetric means, geometric means, harmonic means, medians and midranges. …”
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  15. 15
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…The K-means algorithm requires two inputs for it to be applied onto a data set, the value K, and a proximity measure. …”
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  16. 16
    Determination of the best single imputation algorithm for missing rainfall data treatment

    Determination of the best single imputation algorithm for missing rainfall data treatment by Gamil Abdulraqeb Abdullah Saeed, Chuan, Zun Liang, Roslinazairimah Zakaria, Wan Nur Syahidah Wan Yusoff, Mohd Zuki Salleh

    Published 2016
    “…The proposed algorithms use descriptive measures of the data, including arithmetric means, geometric means, harmonic means, medians and midranges. …”
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  17. 17
    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
    “…It is attained successfully by combining the mean in K-Means algorithm, minimum and maximum in K-Midranges algorithm and compute their average as mean cluster of Hybrid mean. …”
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  18. 18
    Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar

    Comparative analysis of K-Means and K-Medoids for clustering exam questions / Nurul Zafirah Mokhtar by Mokhtar, Nurul Zafirah

    Published 2016
    “…The cluster detection algorithm searches for clusters of data which are similar to one another by using similarity measures. …”
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  19. 19
    Autonomous and deterministic supervised fuzzy clustering

    Autonomous and deterministic supervised fuzzy clustering by Lim, K.M., Loo, C.K., Lim, W.S.

    Published 2010
    “…The model is tested on medical diagnosis benchmark data and Westland vibration data. The results obtained show that the model that uses the global k-means clustering algorithm 1 has higher accuracy when compared to a model that uses the k-means clustering algorithm. …”
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  20. 20
    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
    “…In this paper, we have compared the performance of K-Mean and XMean clustering algorithms using two datasets of student enrollment in higher education institutions. …”
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