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Abstract
The relationship of connectivity between class objects can be defined based on the internal properties of the objects, such as attributes or features, as well as their spatial arrangement or context. The higher the degree of connectivity, the more likely it is that the objects belong to the same class, and conversely, a low degree of connectivity may indicate that the objects belong to different classes.
Machine learning algorithms and methods can be applied to assess and measure the relationship of connectivity between class objects based on available data or features. This can be useful for the automatic analysis of large volumes of data and for supporting decision-making based on the interrelations between class objects
References
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