2021, Vol. 8, No. 1. - go to content...

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DOI: 10.15862/06INOR121 (https://doi.org/10.15862/06INOR121)

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Maximov V.E., Reznikova K.M., Popov D.A. Data mining for marine data analysis. Russian journal of resources, conservation and recycling. 2021; 8(1). Available at: https://resources.today/PDF/06INOR121.pdf (in Russian). DOI: 10.15862/06INOR121

Data mining for marine data analysis

Maximov Valery Evgenievich
Far Eastern federal university, Vladivostok, Russia
E-mail: valep199778@gmail.com

Reznikova Kseniya Mikhailovna
Far Eastern federal university, Vladivostok, Russia
E-mail: a-da_97@mail.ru

Popov Dmitry Alexandrovich
Far Eastern federal university, Vladivostok, Russia
E-mail: dmppda@gmail.com

Abstract. There is practically no industry left where modern information technologies would not be used. Data mining approaches are very popular today. Using this technology allows to transform huge amounts of data into useful information. In the article, the authors present the definition of data mining technology and frequently used methods. Some of the popular data mining techniques include classification, clustering, machine learning, and prediction. The authors paid special attention to such a clustering method as the k-means. The algorithm’s essence is to distribute the dataset into clusters. The finished results can be visualized and detect the scatter by naked eye, which implies heterogeneity in the data. By further investigating these variations, the analyst can find errors and weaknesses in the study area according to the task at hand.

Accurate and complete data is essential in maritime activities. In the field of shipbuilding data analysis and well-made operational decisions can affect the speed and quality of ship construction or even reduce production costs. In shipping and logistics, they can be used to optimize routes and improve the safety of seafarers. Effective use of data mining usually requires highly qualified database specialists and programmers. In this work, the authors have demonstrated a variant of using the Orange Data Mining software tool. This program does not require programming skills from the user, which makes it a useful tool for people far from writing program code.

The article explores the application of the Orange Data Mining program for automated mining of marine data. The results obtained show that the program can be effectively used in maritime activities.

Keywords: Data mining; classification; clustering; prediction; K-means; Orange Data Mining; marine activities

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