Search Results - (("pattern mining algorithm") OR ("based learning algorithm"))

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  1. 1
    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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  2. 2
    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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  3. 3
    IncSPADE: An Incremental Sequential Pattern Mining Algorithm Based on SPADE Property

    IncSPADE: An Incremental Sequential Pattern Mining Algorithm Based on SPADE Property by Omer, Adam, Zailani, Abdullah, Amir, Ngah, Kasypi, Mokhtar, Wan Muhamad Amir, Wan Ahmad, Herawan, Tutut, Noraziah, Ahmad, Mustafa, Mat Deris, Abdul Razak, Hamdan

    Published 2016
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  4. 4
    Sequential pattern mining on library transaction data

    Sequential pattern mining on library transaction data by Sitanggang, Imas Sukaesih, Husin, Nor Azura, Agustina, Anita, Mahmoodian, Naghmeh

    Published 2010
    “…This paper presents results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset. …”
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  5. 5
    A harmony search-based learning algorithm for epileptic seizure prediction

    A harmony search-based learning algorithm for epileptic seizure prediction by Kee, Huong Lai, Zainuddin, Zarita, Ong, Pauline

    Published 2016
    “…The proposed harmony search-based learning algorithm is used in the task of epileptic seizure prediction. …”
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  6. 6
    A modified generalized RBF model with EM-based learning algorithm for medical applications

    A modified generalized RBF model with EM-based learning algorithm for medical applications by Ma, Li Ya, Abdul Rahman, Abdul Wahab, Quek, Chai

    Published 2006
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  7. 7
    Fractional Stochastic Gradient Descent Based Learning Algorithm For Multi-layer Perceptron Neural Networks

    Fractional Stochastic Gradient Descent Based Learning Algorithm For Multi-layer Perceptron Neural Networks by Sadiq, A., Yahya, N.

    Published 2021
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  8. 8
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Dhany, Saputra

    Published 2008
    “…A comprehensive performance study has been reported that PrefixSpan, one of the sequential pattern mining algorithms, outperforms GSP, SPADE, as well as FreeSpan in most cases, and PrefixSpan integrated with pseudoprojection technique is the fastest among those tested algorithms. …”
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  9. 9
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
    “…A comprehensive performance study has been reported that PrefixSpan, one of the sequential pattern mining algorithms, outperforms GSP, SPADE, as well as FreeSpan in most cases, and PrefixSpan integrated with pseudoprojection technique is the fastest among those tested algorithms. …”
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  10. 10
    Case Slicing Technique for Feature Selection

    Case Slicing Technique for Feature Selection by A. Shiba, Omar A.

    Published 2004
    “…CST was compared to other selected classification methods based on feature subset selection such as Induction of Decision Tree Algorithm (ID3), Base Learning Algorithm K-Nearest Nighbour Algorithm (k-NN) and NaYve Bay~sA lgorithm (NB). …”
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  11. 11
    Towards a better feature subset selection approach

    Towards a better feature subset selection approach by Shiba, Omar A. A.

    Published 2010
    “…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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  12. 12
    An experimental evaluation of case slicing as a new classification technique

    An experimental evaluation of case slicing as a new classification technique by Shiba, Omar A. A., Sulaiman, Md. Nasir, Ahmad, Fatimah, Mamat, Ali

    Published 2003
    “…Lastly, it compares the proposed approach with other selected approaches such as the K-Nearest Neighbour (K-NN), Base Learning Algorithm (C4.5) and Naïve Bayes classifier (NB) in solving the classification problems. …”
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  13. 13
    An efficient and effective case classification method based on slicing

    An efficient and effective case classification method based on slicing by Shiba, Omar A. A., Sulaiman, Md. Nasir, Mamat, Ali, Ahmad, Fatimah

    Published 2006
    “…The algorithms are: Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5). The paper also studies the comparison between the proposed method and the two selected classification algorithms using several domains.…”
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  14. 14
    A new text-based w-distance metric to find the perfect match between words

    A new text-based w-distance metric to find the perfect match between words by Ali, M., Jung, L.T., Hosam, O., Wagan, A.A., Shah, R.A., Khayyat, M.

    Published 2020
    “…The k-NN algorithm is an instance-based learning algorithm which is widely used in the data mining applications. …”
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  15. 15
    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
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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  16. 16
    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
    “…To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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  17. 17
    Advances of metaheuristic algorithms in training neural networks for industrial applications

    Advances of metaheuristic algorithms in training neural networks for industrial applications by Chong H.Y., Yap H.J., Tan S.C., Yap K.S., Wong S.Y.

    Published 2023
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  18. 18
    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
    “…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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