Search Results - (( web application mining algorithm ) OR ( based applications use algorithm ))*

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  1. 1
    Web user navigation pattern mining approach based on graph partitioning algorithm.

    Web user navigation pattern mining approach based on graph partitioning algorithm. by Jalali, Mehrdad, Mustapha, Norwati, Mamat, Ali, Sulaiman, Md. Nasir

    Published 2008
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  2. 2
    Web Usage Mining for UUM Learning Care Using Association Rules

    Web Usage Mining for UUM Learning Care Using Association Rules by Azizul Azhar, Ramli

    Published 2004
    “…Application of data mining techniques to the World Wide Web referred as Web mining where this term has been used in three distint ways; Web Content Mining, Web Structure Mining and Web Usage Mining. …”
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  3. 3
    A web usage mining approach based on LCS algorithm in online predicting recommendation systems

    A web usage mining approach based on LCS algorithm in online predicting recommendation systems by Jalali, Mehrdad, Mustapha, Norwati, Sulaiman, Md. Nasir, Mamat, Ali

    Published 2008
    “…To provide online prediction efficiently, we advance an architecture for online predicting in Web Usage Mining system and propose a novel approach based on LCS algorithm for classifying user navigation patterns for predicting users' future requests. …”
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  4. 4
    Expectation maximization clustering algorithm for user modeling in web usage mining system

    Expectation maximization clustering algorithm for user modeling in web usage mining system by Mustapha, Norwati, Jalali, Manijeh, Jalali, Mehrdad

    Published 2009
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  5. 5
    Web usage mining for UUM learning care using association rules

    Web usage mining for UUM learning care using association rules by Ramli, Azizul Azhar

    Published 2004
    “…Application of data milling techniques to the World Wide Web referred as Web mining where this term has been used in three distinct ways; Web Content Mining, Web Structure Mining and Web Usage Mining. …”
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  6. 6
    Genetic Algorithm for Web Data Mining

    Genetic Algorithm for Web Data Mining by Loo, Kevin Teow Aik

    Published 2001
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  7. 7
    A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm.

    A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm. by Jalali, Mehrdad, Mustapha, Norwati, Sulaiman, Md. Nasir, Mamat, Ali

    Published 2009
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  8. 8
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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  9. 9
    An efficient fuzzy clustering algorithm for mining user session clusters on web log data

    An efficient fuzzy clustering algorithm for mining user session clusters on web log data by Mallik, M. A., Zulkurnain, Nurul Fariza

    Published 2021
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  10. 10
    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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  11. 11
    WebPUM : a web-based recommendation system to predict user future movements.

    WebPUM : a web-based recommendation system to predict user future movements. by Jalali, Mehrdad, Mustapha, Norwati, Sulaiman, Md. Nasir, Mamat, Ali

    Published 2010
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  12. 12
    Position score weighting technique for mining web content outliers.

    Position score weighting technique for mining web content outliers. by Mustapha, Norwati, Mustapha, Aida

    Published 2013
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  13. 13
    Efficient prime-based method for interactive mining of frequent patterns.

    Efficient prime-based method for interactive mining of frequent patterns. by Mohammad Hossein, Nadimi Shahraki, Mustapha, Norwati, Sulaiman, Md. Nasir, Mamat, Ali

    Published 2011
    “…During the mining process, the mining algorithm reduces the number of candidate patterns and comparisons by using a new candidate set called candidate head set and several efficient pruning techniques. …”
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  14. 14
    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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  15. 15
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  16. 16
    A Data Mining Approach to Construct Graduates Employability Model in Malaysia

    A Data Mining Approach to Construct Graduates Employability Model in Malaysia by Myzatul Akmam Sapaat, Aida Mustapha, Johanna Ahmad, Khadijah Chamili, Rahamirzam Muhamad

    Published 2024
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  17. 17
    A data mining approach to construct graduates employability model in Malaysia

    A data mining approach to construct graduates employability model in Malaysia by Sapaat, Myzatul Akmam, Mustapha, Aida, Ahmad, Johanna, Chamili, Khadijah, Muhamad, Rahamirzam

    Published 2011
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  18. 18
    Dynamic modeling by usage data for personalization systems

    Dynamic modeling by usage data for personalization systems by Aghabozorgi, S.R., Teh, Y.W.

    Published 2009
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  19. 19
    Mining Web usage using FRS

    Mining Web usage using FRS by Omar, Rosli, Abdullah, Zainatul Shima

    Published 2018
    “…Web Usage Mining (WUM) is the application of data mining methods in extracting potentially useful information from web usage data. …”
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    Proceeding Paper
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  20. 20
    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
    “…Results obtained after pre-processing phase showed that the data quality will improve when the number of records reduced by (51.45). The density-based spatial clustering of applications with noise (DBSCAN) and the K-means algorithm were used to develop clustering algorithms. …”
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