root node

root node

(mathematics, data)
In a tree, a node with no parents, but which typically has daughters.
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Decision trees are the structures consisting of nodes (including a root node and leaf nodes) connected together with branches and generated using the "divide-and-conquer" strategy.
The LMP is the last visited prefix when a trie is traversed from the root node. For example, given a destination address 0100100 (7-bit long for simplicity), the LMP should be the prefix h though the prefix a is also matched.
Although existing research reports absolute figures for the influence of improper design (root node K4) on human error, quantification of the effects of the other three root nodes (insufficient maintenance, K1; inadequate quality control, K2; and improper management, K3) on human error is necessary so that the existing research can be corroborated.
The detection process of the MBS algorithm can be represented by constructing a tree with [N.sub.T] subtrees and 2[N.sub.R] layers, where each subtree has [N.sub.M] complete paths from the root node to the leaf nodes, and each of the paths stands for a candidate solution.
Then, an index vector v of the root node or internal node of the BBT is generated by a top-down approach.
In general, a BST contains a single root node, which contains links to left and right sub-trees.
(i) DIO message: DIO is DODAG Information Object message is disseminated by the root node to initiate DODAG construction.
The topmost node in a classification tree is the root node. The classification starts with the root node and proceeds to the leaves that are most related to the response variable.
In the case of DG trees, the leaf nodes merely sense the data and transmit them to an upstream intermediate node that would in turn aggregate its own data with data received from all of its child nodes and forward the aggregated data to an upstream node that is on the path to the root node of the DG tree.
The tree induction begins with a root node that represents the entire dataset, given dataset and recursively split the data into a few subsets by testing for a given attribute at each level of the node.
The KD-tree method starts a binary searching from the root node in different dimensions.