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  1. 1
    Improvement on agglomerative hierarchical clustering algorithm based on tree data structure with bidirectional approach

    Improvement on agglomerative hierarchical clustering algorithm based on tree data structure with bidirectional approach by Dalbouh H.A., Md Norwawi N.

    Published 2024
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  2. 2
    Development of an intelligent prediction tool for rice yield based on machine learning techniques

    Development of an intelligent prediction tool for rice yield based on machine learning techniques by Md. Sap, Mohd. Noor, Awan, A. M.

    Published 2006
    “…Whereas kernel-based clustering algorithm is developed for finding clusters in climate data. …”
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  3. 3
    Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network

    Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network by Vincent Chung, Norah Tuah, Kit Guan Lim, Min Keng Tan, Ismail Saad, Kenneth Tze Kin Teo

    Published 2020
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  4. 4
    Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield.

    Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. by Md. Sap, Mohd. Noor, Awan, A. Majid

    Published 2005
    “…In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering climate data. …”
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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
    “…Thus, this project proposes a solution to the problems by utilizing the machine learning approach which is the Agglomerative clustering algorithm. Previous studies shows that homogenous grouping of autistics students yields positive results, therefore, this project proposes to design and develop a clustering model system known as the CASDSS (Clustering Autism Spectrum Disorder Students System) where the main goal of this system is to create a homogenous grouping of the ASD students based on their behaviour, skills and intelligence. …”
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  6. 6
    Development of intelligent hybrid learning system using clustering and knowledge-based neural networks for economic forecasting : First phase

    Development of intelligent hybrid learning system using clustering and knowledge-based neural networks for economic forecasting : First phase by Che Mat @ Mohd Shukor, Zamzarina, Md Sap, Mohd Noor

    Published 2004
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  7. 7
    Hybrid intelligent approach for network intrusion detection

    Hybrid intelligent approach for network intrusion detection by Al-Mohammed, Wael Hasan Ali

    Published 2015
    “…Due to the prevailing limitations of finding novel attacks, high false detection, and accuracy in previous intrusion detection approaches, this study has proposed a hybrid intelligent approach for network intrusion detection based on k-means clustering algorithm and support vector machine classification algorithm. …”
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  8. 8
    Minimizing the number of stunting prevalence using the euclid algorithm clustering approach

    Minimizing the number of stunting prevalence using the euclid algorithm clustering approach by Zarlis, Muhammad, Oktavia, Tanty, Buaton, Relita, Ernawan, Ferda, Andrian, Kevin

    Published 2023
    “…The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. …”
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  9. 9
    Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System

    Adaptive Neural Subtractive Clustering Fuzzy Inference System for the Detection of High Impedance Fault on Distribution Power System by Tawafan, Adnan, Sulaiman , Marizan, Ibrahim, Zulkifilie

    Published 2012
    “…This paper proposes an intelligent algorithm using an adaptive neural- Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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  10. 10
    Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering

    Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering by Annisa Eka Haryati, ., Sugiyarto, Surono, Tommy Tanu, Wijaya, Goh, Khang Wen, Aris, Thobirin

    Published 2022
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  11. 11
    A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning And Extended Mobility Features For VANETs

    A Center-Based Stable Evolving Clustering Algorithm With Grid Partitioning And Extended Mobility Features For VANETs by Talib, Mohammed Saad, Abdullah, Nihad Ibrahim, Hassan, Aslinda, Abal Abas, Zuraida, Mohammed Al-Khazraji, Ali Abdul-Jabbar, Alamery, Thamer, Ibrahim, Ali Jalil

    Published 2020
    “…An improvement percentage of the efficiency in (CEC-GP) over the benchmarks Center based stable clustering (CBSC) and evolving data clustering algorithm (EDCA) is 65% and 394% respectively.…”
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  12. 12
    Document clustering using hybrid lda- kmeans

    Document clustering using hybrid lda- kmeans by Foong, O.-M., Ismail, A.N.

    Published 2020
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  13. 13
    Document clustering using hybrid lda- kmeans

    Document clustering using hybrid lda- kmeans by Foong, O.-M., Ismail, A.N.

    Published 2020
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  14. 14
    Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model

    Detecting High Impedance Fault in Power Distribution Feeder with Fuzzy Subtractive Clustering Model by Sulaiman , Marizan, Adnan, Tawafan, Ibrahim, Zulkifilie

    Published 2013
    “…This paper proposes an intelligent algorithm using the Takagi Sugeno- Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect the high impedance fault. …”
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  15. 15
    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
    “…Focusing on clustering algorithms, the study employs popular methods like K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Hierarchical Clustering, and Mean Shift Clustering to understand customer characteristics and behaviors. …”
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  16. 16
    Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling

    Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling by Zulaiha Ali Othman, Afaf Muftah Adabashi, Suhaila Zainudin, Saadat M. Al Hashmi

    Published 2011
    “…This paper presents our work to improve the performance of anomaly intrusion detection using Fuzzy-ART based on the K-means algorithm. The K-means is a modified version of the standard K-means by initializing the value K from the value obtained after data mining using Fuzzy-ART and SNC labeling technique. …”
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  17. 17
    Temporal - spatial recognizer for multi-label data

    Temporal - spatial recognizer for multi-label data by Mousa, Aseel

    Published 2018
    “…Hence, there is a need for a recognition algorithm that can separate the overlapping data points in order to recognize the correct pattern. …”
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  18. 18
    A framework for predicting oil-palm yield from climate data

    A framework for predicting oil-palm yield from climate data by Awan, A. Majid, Md. Sap, Mohd. Noor

    Published 2006
    “…In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering the data. …”
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  19. 19
    Broken Conductor Detection on Power Distribution Feeder

    Broken Conductor Detection on Power Distribution Feeder by Sulaiman , Marizan, Tawafan, Adnan, Ibrahim, Zulkifilie

    Published 2013
    “…It proposes an intelligent algorithm using the Fuzzy Subtractive Clustering Model (FSCM) to detect the high impedance fault. …”
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  20. 20
    High Impedance Fault Detection on Power Distribution Feeder

    High Impedance Fault Detection on Power Distribution Feeder by Sulaiman , Marizan, Tawafan, Adnan, Ibrahim, Zulkifilie

    Published 2012
    “…This paper presents an intelligent algorithm using a Takagi Sugeno-Kang (TSK) fuzzy modeling approach based on subtractive clustering to detect high impedance fault. …”
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