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Data Mining Algorithms for Knowledge Extraction

In: Challenges and Opportunities to Develop Organizations Through Creativity, Technology and Ethics

Author

Listed:
  • Stancu Ana-Maria Ramona

    (“Spiru Haret” University of Bucharest)

  • Cristescu Marian Pompiliu

    (“Lucian Blaga” University of Sibiu)

  • Miglena Stoyanova

    (University of Economics - Varna)

Abstract

In this paper, we study the methods, techniques, and algorithms used in data mining, and from the studied algorithms, we emphasized the clustering algorithms, more precisely on the K-means algorithm. This algorithm was first studied using the Euclidean distance, then modifying the distance between the clusters using the distances Mahalanobis and Canberra. After implementing the algorithms in C/C++, we compared the clustering of the three algorithms, after which we modified them and studied the distance between the clusters.

Suggested Citation

  • Stancu Ana-Maria Ramona & Cristescu Marian Pompiliu & Miglena Stoyanova, 2020. "Data Mining Algorithms for Knowledge Extraction," Springer Proceedings in Business and Economics, in: Silvia L. Fotea & Ioan Ş. Fotea & Sebastian A. Văduva (ed.), Challenges and Opportunities to Develop Organizations Through Creativity, Technology and Ethics, chapter 0, pages 349-357, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-43449-6_20
    DOI: 10.1007/978-3-030-43449-6_20
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    More about this item

    Keywords

    Algorithm; Attribute; Clustering; Matrix; Value;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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