Sentence examples for classification relationship from inspiring English sources

The essence of fuzzy clustering is to construct fuzzy matrix according to the nature of the object itself, on the basis of this, according to a certain degree of membership, to determine the classification relationship.

The essence of cluster analysis is to construct fuzzy matrix based on the attribute of research object itself and, on this basis, determine classification relationship according to certain membership.

Comparing Figs  6 and 7, we found that the classification relationship of these genomes are the same group 1, group 2, group 3, group 4, group 5, and outgroups can still be seen in the two trees as six distinct clusters.

This paper puts forward a feature-based collaborative design system; briefly presents a model of collaborative design and the definition, classification and relationship of the features in this system; discusses key techniques related to realizing feature-based collaborative design in detail; and finally gives the architecture of the feature-based collaborative design system.

Regardless of the plethora of RF genes yet to be identified/characterized, their classification and relationship to the entire extended RF gene superfamily will be easy owing to this nomenclature building block that catalogues newly identified/characterized RF gene products only on the basis of sequence similarity to previously characterized RF gene products.

We developed CARROT (ClAssification of Relationships with ROTations), a new framework for relationship inference that leverages linkage information to differentiate between rotated relationships.

Results: We present CARROT (ClAssification of Relationships with ROTations), a novel framework for relationship inference that leverages linkage information to differentiate between rotated relationships, that is, between relationships with the same number of common ancestors and the same number of meioses separating the individuals under consideration.

The demand for protein sequence- and secondary structure-independent methods for mapping binding pockets has risen recently for the purposes of binding site classification, functional relationships predictions, and prediction of pharmacological intersections [29, 30].

Understanding constraints on phenotypic plasticity is central to explaining its evolution and the evolution of phenotypes in general, yet there is an ongoing debate on the classification and relationships among types of constraints.

The classification and relationships of the five eukaryotic supergroups are still under controversial now [ 54- 57].

For example, in a classical logistic regression, linear combinations are the primary method of expressing the relationships between variables, while in classification trees this relationship does not need to be linear or additive.