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  1. 1
    Improved Malware detection model with Apriori Association rule and particle swarm optimization

    Improved Malware detection model with Apriori Association rule and particle swarm optimization by Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah

    Published 2019
    “…Particle swarm optimization (PSO) is used to optimize the random generation of candidate detectors and parameters associated with apriori algorithm (AA) for features selection. …”
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  2. 2
    Classification with degree of importance of attributes for stock market data mining

    Classification with degree of importance of attributes for stock market data mining by Khokhar, Rashid Hafeez, Md. Sap, Mohd. Noor

    Published 2004
    “…The experimental results show that predictive FDT algorithm can generate a relatively optimal tree without much computation effort (comprehensibility), and WFPRs have a better predictive accuracy of stock market time series data. …”
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  3. 3
    Spatial Data Mining Model For Landfill Sites Suitability Mapping Based On Neural Networks And Multivariate Analysis

    Spatial Data Mining Model For Landfill Sites Suitability Mapping Based On Neural Networks And Multivariate Analysis by Abujayyab, Sohaib K. M.

    Published 2017
    “…Hybrid neural network was utilized as an evaluation method to select the optimal selection method and optimal training algorithm. …”
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  4. 4
    An enhanced sequential exception technique for semantic-based text anomaly detection

    An enhanced sequential exception technique for semantic-based text anomaly detection by Taiye, Mohammed Ahmed

    Published 2019
    “…ESET performs text anomaly detection by employing optimized Cosine Similarity, hybridizing LSA with modified SET, and integrating it with Word Sense Disambiguation algorithms specifically Lesk and Selectional Preference. …”
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  5. 5
    Discovering association rules for mining images datasets: a proposal

    Discovering association rules for mining images datasets: a proposal by Hamzah, Azizi, O. K. Rahmat, Rahmita Wirza, Sulaiman, Md. Nasir

    Published 2005
    “…Our focus for data mining in this paper is concerned with knowledge discovery in image databases. …”
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  6. 6
    Comparative study of apriori-variant algorithms

    Comparative study of apriori-variant algorithms by Mutalib, Sofianita, Abdul Subar, Ammar Azri, Abdul Rahman, Shuzlina, Mohamed, Azlinah

    Published 2016
    “…Handling and processing this “big data” demand powerful data mining methods and analysis tools that can turn data into useful knowledge. …”
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  7. 7
    A frequent pattern mining algorithm based on FP-growth without generating tree

    A frequent pattern mining algorithm based on FP-growth without generating tree by Tohidi, Hossein, Ibrahim, Hamidah

    Published 2010
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  8. 8
    A frequent pattern mining algorithm based on FP-growth without generating tree

    A frequent pattern mining algorithm based on FP-growth without generating tree by Tohid, Hossein, Ibrahim, Hamidah

    Published 2010
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  9. 9
    An initial state of design and development of intelligent knowledge discovery system for stock exchange database

    An initial state of design and development of intelligent knowledge discovery system for stock exchange database by Che Mat @ Mohd Shukor, Zamzarina, Khokhar, Rashid Hafeez, Md Sap, Mohd Noor

    Published 2004
    “…Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules.…”
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  10. 10
    Combining object-based classification and data mining algorithm to classify urban surface materials from worldview-2 satellite image

    Combining object-based classification and data mining algorithm to classify urban surface materials from worldview-2 satellite image by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi

    Published 2014
    “…In this study, Data Mining was performed using C4.5 algorithm to select the appropriate attributes for object-based classification. …”
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  11. 11
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…These tools are known as Data Mining (DM). One aims of DM is to discover decision rules for extracting meaningful knowledge. …”
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  12. 12
    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
    “…The multiple variants in the R-Eclat algorithm generate varied performances in infrequent mining patterns. …”
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  13. 13
    Frequent Lexicographic Algorithm for Mining Association Rules

    Frequent Lexicographic Algorithm for Mining Association Rules by Mustapha, Norwati

    Published 2005
    “…The mined frequent patterns are then used in generating association rules. …”
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  14. 14
    Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images

    Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images by Hamedianfar, Alireza, Mohd Shafri, Helmi Zulhaidi, Mansor, Shattri, Ahmad, Noordin

    Published 2014
    “…The images were used to explore the combined performance of a data mining (DM) algorithm and object-based image analysis (OBIA). …”
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  15. 15
    DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH

    DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH by LUONG, TRUNG TUAN

    Published 2005
    “…The project's objective is identifying the available data mining algorithms in data classification and applying new data mining algorithm to perform classification tasks. …”
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  16. 16
    RARE: mining colossal closed itemset in high dimensional data

    RARE: mining colossal closed itemset in high dimensional data by Md Zaki, Fatimah Audah, Zulkurnain, Nurul Fariza

    Published 2018
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  17. 17
    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
    “…There is potential knowledge inherent in vast amounts of untapped and possibly valuable data generated by healthcare providers. …”
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  18. 18
    Mining dense data: Association rule discovery on benchmark case study

    Mining dense data: Association rule discovery on benchmark case study by Bakar, W.A.W.A., Saman, M.D.M., Abdullah, Z., Jalil, M.A., Herawan, T.

    Published 2016
    “…In this article, we present comparison result between Apriori and FP-Growth algorithm in generating association rules based on a benchmark data from frequent itemset mining data repository. …”
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  19. 19
    Big Data Mining Using K-Means and DBSCAN Clustering Techniques

    Big Data Mining Using K-Means and DBSCAN Clustering Techniques by Fawzia Omer, A., Mohammed, H.A., Awadallah, M.A., Khan, Z., Abrar, S.U., Shah, M.D.

    Published 2022
    “…Many data mining techniques can be used to analyze log files to extract knowledge and valuable information for both organizations and web developers. …”
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  20. 20
    Identifying Relationship between Hearing loss Symptoms and Pure-tone Audiometry Thresholds with FP-Growth Algorithm

    Identifying Relationship between Hearing loss Symptoms and Pure-tone Audiometry Thresholds with FP-Growth Algorithm by G. Noma, Nasir, Mohd Khanapi, Abd Ghani, Mohamad Khir , Abdullah

    Published 2013
    “…The purpose of this study was to find the relationship that exists between pure-tone audiometry threshold values and hearing loss symptoms in a medical datasets of 339 hearing loss patients using association rule mining algorithm. FP-Growth (Frequent Pattern) algorithm is employed for this purpose to generate itemsets given 0.2 (20%) as the support threshold value and 0.7 (70%) as the confidence value for association rule generation. …”
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