Search Results - (( java implementation mining algorithm ) OR ( knowledge applications function algorithm ))

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  1. 1
    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
    “…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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  2. 2
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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  3. 3
    Scalable approach for mining association rules from structured XML data

    Scalable approach for mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  4. 4
    Mining association rules from structured XML data

    Mining association rules from structured XML data by Abazeed, Ashraf Riad, Mamat, Ali, Sulaiman, Md. Nasir, Ibrahim, Hamidah

    Published 2009
    “…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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  5. 5
    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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  6. 6
    Image clustering comparison of two color segmentation techniques

    Image clustering comparison of two color segmentation techniques by Subramaniam, Kavitha Pichaiyan

    Published 2010
    “…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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  7. 7
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  8. 8
    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy

    Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy by Ganesh , Krishnasamy

    Published 2019
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  9. 9
    Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks

    Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks by Krishnasamy, Ganesh, Paramesran, Raveendran

    Published 2019
    “…Experiment results on several multimedia applications have shown that the proposed algorithm is competitive compared with the other single-view feature selection algorithms.…”
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  10. 10
    Ant colony system with heuristic function for the travelling salesman problem

    Ant colony system with heuristic function for the travelling salesman problem by Alobaedy, Mustafa Muwafak, Ku-Mahamud, Ku Ruhana

    Published 2013
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  11. 11
    Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression

    Hybrid Genetic Algorithm based Fuzzy Inference System for Data Regression by Wong S.Y., Siah Yap K., Tan C.H.

    Published 2023
    Conference Paper
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  12. 12
    Dissimilarity algorithm on conceptual graphs to mine text outliers

    Dissimilarity algorithm on conceptual graphs to mine text outliers by Kamaruddin, Siti Sakira, Hamdan, Abdul Razak, Abu Bakar, Azuraliza, Mat Nor, Fauzias

    Published 2009
    “…The graphical text representation method such as Conceptual Graphs (CGs) attempts to capture the structure and semantics of documents.As such, they are the preferred text representation approach for a wide range of problems namely in natural language processing, information retrieval and text mining.In a number of these applications, it is necessary to measure the dissimilarity (or similarity) between knowledge represented in the CGs.In this paper, we would like to present a dissimilarity algorithm to detect outliers from a collection of text represented with Conceptual Graph Interchange Format (CGIF).In order to avoid the NP-complete problem of graph matching algorithm, we introduce the use of a standard CG in the dissimilarity computation.We evaluate our method in the context of analyzing real world financial statements for identifying outlying performance indicators.For evaluation purposes, we compare the proposed dissimilarity function with a dice-coefficient similarity function used in a related previous work.Experimental results indicate that our method outperforms the existing method and correlates better to human judgements. …”
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  13. 13
    The impact of population size on knowledge acquisition in genetic algorithms paradigm: Finding solutions in the game of Sudoku

    The impact of population size on knowledge acquisition in genetic algorithms paradigm: Finding solutions in the game of Sudoku by Abu Bakar, Nordin, Mahadzir, Muhammad Fadhil

    Published 2010
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  14. 14
    Optimized processing of satellite signal via evolutionary search algorithm

    Optimized processing of satellite signal via evolutionary search algorithm by Hassan, Azmi, Othman, Rusli, Tang, Kieh Ming

    Published 2000
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  15. 15
    Fuzzy Type-1 Triangular Membership Function Approximation Using Fuzzy C-Means

    Fuzzy Type-1 Triangular Membership Function Approximation Using Fuzzy C-Means by Azam, M.H., Hasan, M.H., Hassan, S., Abdulkadir, S.J.

    Published 2020
    “…Membership functions determined through statistical and knowledge engineering methods are usually application dependent and cannot be applied on different datasets. …”
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  16. 16
    A shift column different offset for better Rijndael security

    A shift column different offset for better Rijndael security by Mahmod, Ramlan, Ali, Sherif Abdulbari, Abd Ghani, Abdul Azim

    Published 2009
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  17. 17
    An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition

    An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition by Mohd. Hasbullah, Omar

    Published 2011
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  18. 18
    Ciphercrypt-an Encryption System / Nor Hidayah Ngah

    Ciphercrypt-an Encryption System / Nor Hidayah Ngah by Nor Hidayah, Ngah

    Published 2004
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  19. 19
    Normative Fish Swarm Algorithm For Global Optimization With Applications

    Normative Fish Swarm Algorithm For Global Optimization With Applications by Tan, Weng Hooi

    Published 2019
    “…Referred to as Normative Fish Swarm Algorithm (NFSA), the proposed Fish Swarm Algorithm, Optimized by Particle Swarm Optimization with Extended Memory (PSOEM-FSA) is expanded by amalgamating the normative knowledge to provide supplementary guidelines for better global optimum achievement and convergence rate. …”
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  20. 20
    Statistical analysis, ciphertext only attack, improvement of generic quasigroup string transformation and dynamic string transformation

    Statistical analysis, ciphertext only attack, improvement of generic quasigroup string transformation and dynamic string transformation by Hassan Disina, Abdulkadir, Jamel, Sapiee, Abubakar Pindar, Zahraddeen, Mat Deris, Mustafa

    Published 2018
    “…Quasigroup String transformation is one of those primitives that have many applications in cryptographic algorithms, Hash functions, and Pseudo-Random Number Generators. …”
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