Performance Analysis of Data Mining, Neural networks &SVM and Fuzzy Algebra in Decision Making Process for Machine Learning Dataset

  • Authors

    • D Sukheja
    • Kriti Ohri
    • V Baby
    • Tranjeet Sood
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.28.28345
  • Data, data collection techniques, data mining techniques and knowledge mining process, machine learning, eigen vectors, interval valued fuzzy matrix, decision making.

  • Abstract

    The way towards mining to information and knowledge from the gigantic information has been composed by a few research analysts as a core research territory in database frameworks, information warehouse and mining, big data and machine learning. Furthermore, the procedure of information and knowledge mining has been used by various sorts of associations with an opportunity to generate the better revenues and expansions of their business by anticipating the future scenario. In present situation, knowledge mining process is a subset of machine learning, in the view of the fuzzy interval approach. Interval Eigen problem of interval fuzzy matrices in max-min algebra are explored. The portrayal of interval eigenvectors which has been exhibited in (Gavalec, Plávka, & Tomášková, in print) is utilized in decision making. Decision making is vital role in terms of machine learning or imparting artificial intelligence into machines which work upon the traditional logic hypothesis. It is a progression which assists a machine to imagine like a human being and for a human being to facilitate his/her distinctive decision making process.This paper describes the knowledge based decision making process and demonstrates a decision support system using fuzzy interval approach.

     

     

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  • How to Cite

    Sukheja, D., Ohri, K., Baby, V., & Sood, T. (2018). Performance Analysis of Data Mining, Neural networks &SVM and Fuzzy Algebra in Decision Making Process for Machine Learning Dataset. International Journal of Engineering & Technology, 7(4.28), 689-698. https://doi.org/10.14419/ijet.v7i4.28.28345

    Received date: 2019-03-14

    Accepted date: 2019-03-14

    Published date: 2018-11-30