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  1. 1
    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
    “…The advantage of the cluster labelling algorithm compared to co-spectral plot and maximum-likelihood classifier was the algorithm provided a rapid production of high accuracy classification map.…”
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  2. 2
    Aco-based feature selection algorithm for classification

    Aco-based feature selection algorithm for classification by Al-mazini, Hassan Fouad Abbas

    Published 2022
    “…An enhanced graph clustering ant colony optimisation (EGCACO) algorithm is proposed to solve the three (3) MGCACO algorithm problems. …”
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  3. 3
    Combining cluster quality index and supervised learning to predict students’ academic performance

    Combining cluster quality index and supervised learning to predict students’ academic performance by Suhaila Zainudin, Rapi’ah Ibrahim, Hafiz Mohd Sarim

    Published 2024
    “…Then, the clusters were evaluated with cluster quality indexes, namely, the Silhouette Coefficient, Calinski-Harabasz Index and Davies-Bouldin Index, to determine the best clusters. …”
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  4. 4
    Effective gene selection techniques for classification of gene expression data

    Effective gene selection techniques for classification of gene expression data by Yeo, Lee Chin

    Published 2005
    “…The selected subset of genes is then be used to train the classifiers for constructing rules for future tissue classification problem. Various k-means clustering algorithms and model-based clustering algorithms are proposed to group the genes. …”
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  5. 5
    Clustering network traffic utilization

    Clustering network traffic utilization by Mohd Khairudin, Nazli, Muda, Zaiton, Mustapha, Aida, Nagarathinam, Yogeswaran, Salleh, Mohd. Sidek

    Published 2013
    “…The analysis discussed on the quality of resulting clusters from all the algorithms. …”
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  6. 6
    Determining the preprocessing clustering algorithm in radial basis function neural network

    Determining the preprocessing clustering algorithm in radial basis function neural network by S.L. Ang, H.C. Ong, H.C. Law

    Published 2008
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  7. 7
    Hybrid 2D phase unwrapping methodology using spatial fuzzy clustering and neurofuzzy classification with application in sar images

    Hybrid 2D phase unwrapping methodology using spatial fuzzy clustering and neurofuzzy classification with application in sar images by Then, Patrick Hang Hui.

    Published 2001
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  8. 8
    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms

    Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms by Alswaitti, Mohammed Y. T.

    Published 2018
    “…The overall performances of the three proposed frameworks have been compared with several current state-of-the-art clustering algorithms on 15 benchmark datasets from the UCI repository. …”
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  9. 9
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…However, data is rarely perfect and there are many inconsistencies that affect data quality such as noise data. Nowadays, the use of SVM is very perspective for the big data classification. …”
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  10. 10
    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem

    A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

    Published 2021
    “…However, data is rarely perfect and there are many inconsistencies that affect data quality such as noise data. Nowadays, the use of SVM is very perspective for the big data classification. …”
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  11. 11
    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi

    Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

    Published 2019
    “…Based on the above components and circumstances, many studies have been performed on data clustering problems. Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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  12. 12
    Development of a parallel clustering of bilingual corpora based on reduced terms

    Development of a parallel clustering of bilingual corpora based on reduced terms by Leow, Ching Leong

    Published 2015
    “…The quality of clustering bilingual text documents is highly influenced by the quality of the bag-of-word presentation of Malay text articles presented to the clustering algorithm. …”
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  13. 13
    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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  14. 14
    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm by Taha, Zahari, Razman, Mohd Azraan, Abdul Ghani, Ahmad Shahrizan, P.P. Abdul Majeed, Anwar, Musa, Rabiu Muazu, A. Adnan, Fatihah, Sallehudin, Muhammad Firdaus, Mukai, Yukinori

    Published 2018
    “…The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. …”
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    Proceeding Paper
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  15. 15
    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

    The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm by Zahari, Taha, M. A. M., Razman, Ahmad Shahrizan, Abdul Ghani, A. P. P., Abdul Majeed, R. M., Musa, F. A., Adnan, M. F., Sallehudin, Y., Mukai

    Published 2018
    “…The clustered fish behaviour is then classified through k-Nearest Neighbour (k-NN) learning algorithm. …”
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  16. 16
    MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM

    MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM by Aurangzeb, khan, Baharum, Baharudin, Khairullah, khan

    Published 2011
    “…Dead-Stock (DS), Slow-Moving (SM) and Fast- Moving (FM) using K-means algorithm. In the second phase we have proposed Most Frequent Pattern (MFP) algorithm to find frequencies of property values of the corresponding items. …”
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    Citation Index Journal
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  17. 17
    Parallel execution of distributed SVM using MPI (CoDLib)

    Parallel execution of distributed SVM using MPI (CoDLib) by Salleh N.S.M., Suliman A., Ahmad A.R.

    Published 2023
    “…Support Vector Machine (SVM) is an efficient data mining approach for data classification. However, SVM algorithm requires very large memory requirement and computational time to deal with very large dataset. …”
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  18. 18
    Intelligent Color Vision System For Ripeness Classification Of Oil Palm Fresh Fruit Bunch

    Intelligent Color Vision System For Ripeness Classification Of Oil Palm Fresh Fruit Bunch by Fadilah, Norasyikin

    Published 2015
    “…The images are collected and analyzed using digital image processing techniques. k-means clustering algorithm is used to segment the image into two separate regions which are fruit and spike regions. …”
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  19. 19
    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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  20. 20
    Classification of capsicum leaf disease from a complex cluster of leaves using an improved multiple layers ShuffleNet CNN model

    Classification of capsicum leaf disease from a complex cluster of leaves using an improved multiple layers ShuffleNet CNN model by Chyntia Jaby, Entuni, Tengku Mohd Afendi, Zulcaffle, Kismet, Hong Ping

    Published 2023
    “…These models have achieved an average accuracy of classification. However, classifying diseases becomes relatively challenging when a diseased leaf grows alongside a cluster of other leaves. …”
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