Akademik Veri Yönetim Sistemi
The International İskenderun Bay Symposium, Hatay, Türkiye, 11 - 13 Ekim 2017, ss.68
Self-Organizing Maps (SOM), a type of Artificial
Neural Network (ANN), is a data clustering tool that provides a way of
representing multi-dimensional data in two-dimensional space. The maps are
produced preserving topological relations between parameters of the input
vectors. Unlike multi-layered feed forward neural networks, SOM employs
unsupervised learning training mechanism. Interestingly, it requires no prior
knowledge regarding the solution. The variety of the applications which employ
SOM for data analysis reported in the literature is a clear indication of its
acceptance as a powerful data analysis tool. SOM may, not only, present better
viewing opportunities in such cases that displaying the relationships between
the factors effecting the problem is impossible, but also, provides better
exploration of the data. Application of SOM on the data collected in fisheries
science provided enhanced outcomes and better understanding on the data
collected. In this paper, SOM is discussed and
reviewed in view of aquaculture and fisheries research based on the
prevalence of isopods in the buccal cavity of one grouper species. The research
was carried out to determine the seasonal patterns and potential impacts of the
parasites on the goldblotch grouper using the SOM which
were conducted in Iskenderun Bay.