Abstract
Recent developments in information management and corporate computerization of company processes have made data processing faster, easier, and more accurate. Data mining and machine learning techniques have been used increasingly in different areas, from medical to technological, training, and energy applications to analyze data. Machine learning techniques allow significant additional knowledge to be deducted from data processed by data mining. This critical and practical knowledge allows companies to develop their plans on a sound basis and reap substantial time and expense benefits. This article implements the classification methods used for data mining and computer training for the collected data during technical advice processes and aims to find the most powerful algorithm.
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References
M. Gera, S. Goel, Data Mining—techniques, methods and algorithms: a review on tools and their validity international. J. Comput Appl. 113(18), 0975–8887 (March 2015)
Ö. Ünsal, Determination of Vocational Fields With Machine Learning Algorithm, M.Sc. Thesis (Gazi University, Ankara, Turkey, 2011)
I.H. Witten, E. Frank, Practical Machine Learning Tools and Techniques, 2nd edn. (Morgan Kaufmann Publications, USA, 2005).
S. Özekes, Data mining models and their applications. J. Istanbul Commerce Univ. 3, 65–82 (2003) (Stanbul, Turkey)
M.T. Mitchell, Machine Learning (McGraw-Hill, USA, 2016).
Y. Özkan, Data Mining Methods (Papatya Publications, Stanbul, Turkey, 2008)
A.S. Albayrak, K. Ylmaz, Data mining: decision tree algorithms and an application on MKB data. J. Suleyman Demirel University, Facul of Econom Administrative Sci. 14(1), 31–52 (2009) (Isparta, Turkey)
M.F. Amasyal, Introduction to Machine Learning (2010). https://www.ce.yildiz.edu.tr/mygetfile.php?id=868
G. Silahtarolu, Basic Concepts and Algorithms of Data Mining (Papatya Publishing, Stanbul, Turkey, 2008)
N. Murat, The Use Of Bayesian Approaches To Model Selection, M.Sc. Thesis (Ondokuz Mays University, Samsun, Turkey, 2007)
A.L. Buczak, E. Guven, A survey of data mining and machine learning methods for cyber security intrusion detection (2019)
S. Sharma, J. Agrawal, S. Agarwal, S. Sharma, Machine Learning techniques for data mining: a survey (2018)
Z. Huang, Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. (Acsys CRC, CSIRO, 2018)
U. Fayyad, G.P. Shapiro, P. Smyth, The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39, 27–34 (2017)
N.E. Alpayd, Introduction to Machine Learning (The MIT Press, London, England, 2016)
M.F. Amasyal New Machine Learning Methods and Drug Design Applications, Ph.D. Thesis (Yldz Technical University, Stanbul, Turkey, 2016)
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Bedi, P., Goyal, S.B., Kumar, J. (2021). Applied Classification Algorithms Used in Data Mining During the Vocational Guidance Process in Machine Learning. In: Suma, V., Chen, J.IZ., Baig, Z., Wang, H. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1395-1_11
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DOI: https://doi.org/10.1007/978-981-16-1395-1_11
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