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The larger the degree of significance a tree-like pattern has, the more the tree-like pattern is compacted in the sequence.
A decision tree algorithm constructs a tree-like classification model which consists of nodes, branches and leaves.
A decision tree is a tree-like decision support tool that uses a graph to convey decisions and their possible consequences (Adamo 1980; Yuan and Shaw 1995).
The pathways have a tree-like appearance and each decision tree has approximately 40 branches.
However, as the methods applied here yield partitions, the communities of a tree-like network are also necessarily tree-like.
A Decision Tree is a tree-like structure with leaves representing classifications and branches representing the combinations of feature values that lead to those classifications.
So it is instead specifically (1) that descent with modification is a predominantly tree-like process and (2) that this tree-like process produces a tree-like pattern, that together make up Darwin's hypothesis.
These approaches, aiming to produce a tree-like pattern whether the tree is the right model for representing evolution or not, are derived from a tree-thinking perspective.
Both network methods have the attractive property that they produce the set of splits corresponding to the correct tree when given a tree-like matrix.
This produces a tree-like construct (known as a decision tree) that, after being optimized, can be used to categorize new samples.
Therefore, we developed a graph-theoretical framework that enables automatic construction of a tree-like representation, analogous to the clique tree representation, for much broader family of graphs.
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