Search Results - (( data distribution mining algorithm ) OR ( _ variation ((bees algorithm) OR (new algorithm)) ))

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  1. 1
    Deterministic and stochastic inventory routing problems with backorders using artificial bee colony / Huda Zuhrah Ab Halim

    Deterministic and stochastic inventory routing problems with backorders using artificial bee colony / Huda Zuhrah Ab Halim by Huda Zuhrah , Ab Halim

    Published 2019
    “…The inventory holding cost is assumed to be product specific and only incurred at the assembly plant. An Artificial Bee Colony (ABC) algorithm is proposed for the problem. …”
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  2. 2
    Satisfiable Integer Programming Algorithm On Distributed Inter Process Communication (SIP-DIPC)

    Satisfiable Integer Programming Algorithm On Distributed Inter Process Communication (SIP-DIPC) by Abdul Hamid, Mohd Hakim, Abu, Nur Azman, Mohamad, Siti Nurul Mahfuzah, Idris, Aris, Zakaria, Zahriladha, Sulaiman, Zuraidah

    Published 2019
    “…Data Analytics is a superset to Data Mining. Data mining algorithm is getting popular support in recent development of Big Data. …”
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  3. 3
    Experimental study of variation local search mechanism for bee algorithm feature selection

    Experimental study of variation local search mechanism for bee algorithm feature selection by Mahmuddin, Massudi, Al-dawoodi, Aras Ghazi Mohammed

    Published 2017
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  4. 4
    Discovery of SIP/DRIP approach in distributed inter process communication

    Discovery of SIP/DRIP approach in distributed inter process communication by Hamid H., Jais J.

    Published 2023
    “…Classification modeling in data mining has evolved since 1990's. Many methods have been introduced and experimented. …”
    Conference paper
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  5. 5
    A neural network modal decomposition mechanism in predicting network traffic

    A neural network modal decomposition mechanism in predicting network traffic by Shi Jinmei

    Published 2023
    “…This study designs a novel network traffic prediction model namely SAVE-AS. It embeds a new proposed Scalable Artificial Bee Colony (SABC) algorithm, Phase Space Reconstruction, Variational Mode Decomposition (VMD) and an integrated Extreme Learning Machine (ELM). …”
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  6. 6
    Analysis using data mining techniques: the exploration and review data of diabetes patients / Syarifah Adilah Mohamed Yusoff ... [et al.]

    Analysis using data mining techniques: the exploration and review data of diabetes patients / Syarifah Adilah Mohamed Yusoff ... [et al.] by Mohamed Yusoff, Syarifah Adilah, Othman, Jamal, Johan, Elly Johana, Mohd Mydin, Azlina, Wan Mohamad, Wan Anisha

    Published 2025
    “…In this statistical summary procedure, the distribution of attributes and their interactions are crucial for accurately processing the data in accordance with the selected classification or data mining techniques to be performed. …”
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  7. 7
    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
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  8. 8
    Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set

    Predicting Customer Buying Decisions for Online Shopping with Unbalanced Data Set by Yap, Chau Tean

    Published 2022
    “…Weka, a data mining tool, provides the facility to classify the data set with different machine learning algorithms. …”
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  9. 9
    Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control

    Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control by Naidu, K., Mokhlis, Hazlie, Bakar, Ab Halim Abu

    Published 2014
    “…Furthermore, the proposed algorithm is robust enough to operate under different operating conditions and system parameter variations.…”
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  10. 10
    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
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  11. 11
    Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability

    Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability by Romli, Nurul Atiqah, Zulkepli, Nur Fariha Syaqina, Kasihmuddin, Mohd Shareduwan Mohd, Zamri, Nur Ezlin, Rusdi, Nur 'Afifah, Manoharam, Gaeithry, Mansor, Mohd. Asyraf, Jamaludin, Siti Zulaikha Mohd, Malik, Amierah Abdul

    Published 2024
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  12. 12
    Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo

    Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo by Terence, Yong Koon Beh, Swee, Chuan Tan, Hwee, Theng Yeo

    Published 2014
    “…Building classification models from such imbalanced data sets is a relatively new challenge in the machine learning and data mining community because many traditional classification algorithms assume similar proportions of majority and minority classes. …”
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  13. 13
    Binary vote assignment on grid quorum replication technique with association rule

    Binary vote assignment on grid quorum replication technique with association rule by Ainul Azila, Che Fauzi

    Published 2018
    “…Performance of the BVAGQ-AR technique comprised the following steps. First step is mining the data by using Apriori algorithm from Association Rules. …”
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  14. 14
    A novel approach to data mining using simplified swarm optimization

    A novel approach to data mining using simplified swarm optimization by Wahid, Noorhaniza

    Published 2011
    “…Data mining has become an increasingly important approach to deal with the rapid growth of data collected and stored in databases. …”
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  15. 15
    Logistic regression methods for classification of imbalanced data sets

    Logistic regression methods for classification of imbalanced data sets by Santi Puteri Rahayu, -

    Published 2012
    “…Classification of imbalanced data sets is one of the important researches in Data Mining community, since the data sets in many real-world problems mostly are imbalanced class distribution. …”
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  16. 16
    An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis

    An Integrated Principal Component Analysis And Weighted Apriori-T Algorithm For Imbalanced Data Root Cause Analysis by Ong, Phaik Ling

    Published 2016
    “…A semiconductor manufacturing case study with Work In Progress data and true alarm data is used to proof the proposed algorithm. …”
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  17. 17
    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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  18. 18
    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets

    Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

    Published 2019
    “…Classification of imbalanced datasets remained a significant issue in data mining and machine learning (ML) fields. This research work proposed a new idea based on the optimization for handling the imbalanced datasets. …”
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  19. 19
    Sequential pattern mining using personalized minimum support threshold with minimum items

    Sequential pattern mining using personalized minimum support threshold with minimum items by Alias, Suraya, Razali, Mohd Norhisham, Tan, Soo Fun, Sainin, Mohd Shamrie

    Published 2011
    “…One of the challenges of Sequential Pattern Mining is finding frequent sequential patterns in a huge click stream data (web logs) since the data has the issue of a very low support distribution.By applying a Frequent Pattern Discovery technique, a sequence is considered as frequent if it occurs more than the minimum support (min sup) threshold value.The conventional method of assuming one min sup value is valid for all levels of k-sequence, may have an impact on the overall results or pattern generation. …”
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    Conference or Workshop Item
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  20. 20
    Optimal variational mode decomposition and integrated extreme learning machine for network traffic prediction

    Optimal variational mode decomposition and integrated extreme learning machine for network traffic prediction by Jinmei Shi, Yu-Beng Leau, Kun Li, Huandong Chen

    Published 2021
    “…A Scalable Artificial Bee Colony (SABC) algorithm which has fewer adjustable parameters and can thus guarantee the accuracy and stability of the prediction mechanism is also proposed. …”
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