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  1. 1
    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
    “…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.…”
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  2. 2
    Frequent Lexicographic Algorithm for Mining Association Rules

    Frequent Lexicographic Algorithm for Mining Association Rules by Mustapha, Norwati

    Published 2005
    “…The primary concept of association rule algorithms consist of two phase procedure. In the first phase, all frequent patterns are found and the second phase uses these frequent patterns in order to generate all strong rules. …”
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  3. 3
    Modifying iEclat algorithm for infrequent patterns mining

    Modifying iEclat algorithm for infrequent patterns mining by Julaily Aida, J., Mustafa, M.

    Published 2018
    “…This paper proposes an enhancement algorithm based on iEclat algorithms for mining infrequent pattern.…”
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  4. 4
    Frequent patterns minning of stock data using hybrid clustering association algorithm

    Frequent patterns minning of stock data using hybrid clustering association algorithm by B., Baharudin, A., Khan, K., Khan

    Published 2009
    “…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data. …”
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  5. 5
    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
    “…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.…”
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  6. 6
    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition

    Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition by Leong, Shi Xiang

    Published 2017
    “…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). Phase 2 is to study the performance of proposed integration system which using the data clustered to be used as train data for Naïve Bayes classifier. …”
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  7. 7
    Pattern Recognition for Human Diseases Classification in Spectral Analysis

    Pattern Recognition for Human Diseases Classification in Spectral Analysis by Nur Hasshima Hasbi, Abdullah Bade, Fuei, Pien Chee, Muhammad Izzuddin Rumaling

    Published 2022
    “…One common problem in pattern recognition is dealing with multidimensional data, which is prominent in studies involving spectral data such as ultraviolet visible (UV/Vis), infrared (IR), and Raman spectroscopy data. …”
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  8. 8
    Modifying iEclat algo ithm for infrequent patterns mining

    Modifying iEclat algo ithm for infrequent patterns mining by Julaily Aida, Jusoh, Mustafa, Man

    Published 2018
    “…This paper proposes an enhancement algorithm based on iEclat algorithms for mining infrequent pattern.…”
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  9. 9
    Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset

    Performance of IF-Postdiffset and R-Eclat Variants in Large Dataset by Julaily Aida, Jusoh, Wan Aezwani, Wan Abu Bakar, Mustafa, Man

    Published 2018
    “…Pattern mining refers to a subfield of data mining that uncovers interesting, unexpected, and useful patterns from transaction databases. …”
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  10. 10
    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer

    Autonomous anomaly detection using density-based features in streaming data / Muhammmad Yunus Iqbal Basheer by Iqbal Basheer, Muhammmad Yunus

    Published 2023
    “…Hence, it is critical for an anomaly detection algorithm to detect data anomalies patterns. Nonetheless, the process of detecting anomalies in streaming data is laborious. …”
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  11. 11
    A numerical method for frequent pattern mining

    A numerical method for frequent pattern mining by Mustapha, Norwati, Nadimi-Shahraki, Mohammad-Hossein, Mamat, Ali, Sulaiman, Md. Nasir

    Published 2009
    “…Frequent pattern mining is one of the active research themes in data mining. …”
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  12. 12
    Pattern discovery using k-means algorithm

    Pattern discovery using k-means algorithm by Ahmed, Almahdi Mohammed, Wan Ishak, Wan Hussain, Md Norwawi, Norita, Alkilany, Ahmed

    Published 2014
    “…This paper will discuss the results of a pattern extraction process using a clustering algorithm that is k-means. …”
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  13. 13
    The development of an automated pattern recognition based on neural network / Irni Hamiza Hamzah, Mohammad Nizam Ibrahim and Linda Mohd Kasim

    The development of an automated pattern recognition based on neural network / Irni Hamiza Hamzah, Mohammad Nizam Ibrahim and Linda Mohd Kasim by Hamzah, Irni Hamiza, Ibrahim, Mohammad Nizam, Mohd Kasim, Linda

    Published 2006
    “…Pattern recognition techniques are used to automatically classify a variety of physical objects or abstract patterns. …”
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  14. 14
    An expanded square pattern technique in swarm of quadcopters for exploration algorithm

    An expanded square pattern technique in swarm of quadcopters for exploration algorithm by Zuhri, Muhammad Fuad Reza, Ismail, Amelia Ritahani

    Published 2017
    “…We simulate the swarm-based exploration algorithm with expanded square pattern using a VREP simulator. …”
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  15. 15
    Pattern Discovery Using K-Means Algorithm

    Pattern Discovery Using K-Means Algorithm by Ahmed, AM, Norwawi, NM, Ishak, WHW, Alkilany, A

    Published 2024
    “…This paper will discuss the results of a pattern extraction process using a clustering algorithm that is k-means. …”
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  16. 16
    Finding spatio-temporal patterns in climate data using clustering

    Finding spatio-temporal patterns in climate data using clustering by Md. Sap, Mohd. Noor, Awan, A. Majid

    Published 2005
    “…The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data.…”
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  17. 17
    Advanced flood prediction at forest with rainfall data using various machine learning algorithms

    Advanced flood prediction at forest with rainfall data using various machine learning algorithms by M.S., Saravanan, S., Sivashankar, A., Rajesh, Mat Ibrahim, Masrullizam

    Published 2024
    “…The aim is to classify and predict floods in advance with rain data patterns of India using spatio-temporal logic. …”
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  18. 18
    An Efficient Data Structure for General Tree-Like Framework in Mining Sequential Patterns Using MEMISP

    An Efficient Data Structure for General Tree-Like Framework in Mining Sequential Patterns Using MEMISP by Saputra, Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2007
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  19. 19
    Sheet C1 Pattern Number Recognition Using Artificial Immune System Optimization

    Sheet C1 Pattern Number Recognition Using Artificial Immune System Optimization by Yuniarti Denita, Sari, Tri Basuki, Kurniawan, Edi Surya, Negara, Tata, Sutabri

    Published 2022
    “…The problem was that the KPPS (Voting Committee Group) failed to clearly write the legitimate vote acquisition for each TPS on sheet C1, which caused difficulty for the KPU employees when entering the data. In order to recognize the numerical patterns on sheet C1, we, therefore, require a method that uses the Artificial Immune System (AIS), specifically the CSA (Clonal Selection Algorithm) algorithm, which is one of the AIS system's algorithms for optimization. …”
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  20. 20
    Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm

    Discovering Pattern in Medical Audiology Data with FP-Growth Algorithm by G. Noma, Nasir, Mohd Khanapi, Abd Ghani

    Published 2012
    “…In this paper we propose a five step knowledge discovery model to discover patterns in medical audiology records. We use frequent pattern growth (FP-Growth) algorithm in the data processing step to build the FP-tree data structure and mine it for frequents itemsets. …”
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