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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
    A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption

    A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption by Chiroma, H., Abdullahi, U.A., Hashem, I.A.T., Saadi, Y., Al-Dabbagh, R.D., Ahmad, M.M., Dada, G.E., Danjuma, S., Maitama, J.Z., Abubakar, A., Abdulhamid, S.�M.

    Published 2019
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  9. 9
    Dimensionality reduction in data summarization approach to learning relational data

    Dimensionality reduction in data summarization approach to learning relational data by Kheau, Chung Seng, Rayner Alfred, Lau, Hui Keng

    Published 2013
    “…Based on the experimental results, the DARA algorithm is proven to be very effective in learning relational data. …”
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  10. 10
    A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption

    A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption by Haruna, Chiroma, Abdullahi, Usman Ali, Targio Hashem, Ibrahim Abaker, Saadi, Younes, Al-Dabbagh, Rawaa Dawoud, Ahmad, Muhammad Murtala, Emmanuel Dada, Gbenga, Danjuma, Sani, Maitama, Jaafar Zubairu, Abubakar, Adamu, Abdulhamid, Shafi’i Muhammad

    Published 2019
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  11. 11
    An ensemble data summarization approach based on feature transformation to learning relational data

    An ensemble data summarization approach based on feature transformation to learning relational data by Chung, Seng Kheau

    Published 2015
    “…In the process of summarizing data, the data stored in multi-relational database need to be transformed into Term Frequency - Inverse Document Frequency (TF-IDF) vector space. …”
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  12. 12
    An evolutionary based features construction methods for data summarization approach

    An evolutionary based features construction methods for data summarization approach by Rayner Alfred, Suraya Alias, Chin, Kim On

    Published 2015
    “…In other words, this research will discuss the application of genetic algorithm to optimize the feature construction process from the Coral Reefs data to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). …”
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  13. 13
    Design and analysis of management platform based on financial big data

    Design and analysis of management platform based on financial big data by Chen, Yuhua, Mustafa, Hasri, Zhang, Xuandong, Liu, Jing

    Published 2023
    “…In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. …”
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  14. 14
    An adaptive data gathering algorithm for minimum travel route planning in WSNs based on rendezvous points

    An adaptive data gathering algorithm for minimum travel route planning in WSNs based on rendezvous points by Ghaleb, Mukhtar, K. Subramaniam, Shamala, Ghaleb, Safwan Mahmood Yahya

    Published 2019
    “…MCRP exhibits a relatively close performance to other algorithms with respect to power algorithms.…”
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  15. 15
    Survey on input output relation based combination test data generation strategies

    Survey on input output relation based combination test data generation strategies by Alsewari, Abdulrahman A., Tairan, Nasser M., Kamal Z., Zamli

    Published 2015
    “…Benchmarking results illustrate the comparative performance of existing nature inspired algorithm based strategies supporting IOR.…”
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  16. 16
    Predicting future sales using genetic algorithm: data mining sales / Siti Hajar Yaacob

    Predicting future sales using genetic algorithm: data mining sales / Siti Hajar Yaacob by Yaacob, Siti Hajar

    Published 2014
    “…Besides that, Genetic Algorithm is applying to predict future sales based on the sales and product data. …”
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  17. 17
    Clustering algorithm for market-basket analysis : the underlying concept of data mining technology

    Clustering algorithm for market-basket analysis : the underlying concept of data mining technology by Abdul Kadir, Khairil Annuar

    Published 2003
    “…The clustering algorithm based on Small Large Ratios, SLR is presented in a manner that helps to understand the concept of data mining technology in marketbasket analysis. …”
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  18. 18
    Survey on input output relation based combination test data generation strategies

    Survey on input output relation based combination test data generation strategies by Alsewari, Abdulrahman A., Tairan, Nasser M., Kamal Z., Zamli

    Published 2015
    “…Benchmarking results illustrate the comparative performance of existing nature inspired algorithm based strategies supporting IOR.…”
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  19. 19
    Case Slicing Technique for Feature Selection

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

    Published 2004
    “…Since the 1960s, many algorithms for data classification have been proposed. …”
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  20. 20
    Normalized Relational Storage for Extensible Markup Language (XML) Schema

    Normalized Relational Storage for Extensible Markup Language (XML) Schema by Kamsuriah, Ahmad, Reduan, Samad

    Published 2011
    “…The algorithm is based on computing a set of minimum covers for all functional dependencies on a universal relation when given XML Functional Dependencies (XFDs) and the schema information. …”
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