## Definition of Tree Traversal Techniques

Tree traversal techniques are very important traversal techniques in computer science that can resolve many of the graph-based problems in real life. Tree traversal involves traversing each node of the tree at least once where the information in the form of key and value can be retrieved for easy identification. Tree traversal is a recursive process where the scenario might arise to visit the same node again therefore to avoid the situation a stack is maintained either in the form of a queue or stack to maintain the hierarchy of nodes within.

### Tree Traversal Techniques

Tree traversal techniques play a significant role when it comes to computer science and graph theory concepts to be applied to real-life scenarios. Traversal in a tree within nodes is possible when certain operations are applied. The operations applied on these sub-trees are known as tree traversal techniques.

In order to get the tree traversal for Binary Tree Traversal Techniques four possible ways:

– Inorder Tree traversal

– Preoder Tree traversal

– Postorder Tree traversal

– Levelorder Tree traversal

In order to get the tree traversal for Recursive Traversal algorithms are as follows:

– Inorder Tree Traversal

– Preorder Tree Traversal

– Postorder Tree Traversal

In order to get the tree traversal for Non Recursive traversal algorithms are similar as above just what is different and segregated is as follows:

Level Order Tree Traversal

#### # Binary Tree Traversal Techniques

Binary tree traversal techniques include these operations when in case there are scenarios related to them.

The inorder traveral, in this case, is the same where the left subtree of node traversed before right subtree and a major difference can be seen on the root of the entire tree.

In the case of preorder and postorder traversal also the behavior is the same as in the order traversal technique.

#### # Recursive Tree Traversal Techniques

Recursive Tree Traversal Techniques also involve inorder traversal technique in which the root of each subtree is visited once the left subtree is traversed with but before the right tree traversal gets initiated. For inorder traversal with respect to recursive tree, traversal involves certain procedures like :

At first when inorder traversal is followed then in that case the left subtree is visited followed by inorder traversal then manipulation is performed over the root and then finally the right subtree is visited for further manipulation.

Recursive tree traversal with respect to preorder traversal involves visiting each of the tree’s roots once the left subtrees and right subtrees traversal is covered. Procedures followed are in a way where the root is visited first then the left subtree is visited followed by the right subtree thus covering the entire preorder traversal.

In the case of Postorder traversal with respect to recursive tree traversal techniques is as follows where the root is visited only once the left subtree and right subtree is covered for traversal. Thus procedure to cover the postorder traversal includes a visit to the first left sub-tree then a visit the right subtree to cover and once done then it lands to the last node which is the root.

Level order tree traversal is also one of the traversal techniques which are quite different when compared to Preorder, Inorder, and Postorder traversal technique where the traversal has a pattern that takes place level by level which gets initiated from the root and then travels from left to right where each traversal within a level requires a data structure as mentioned before queue or stack.

When a queue is present as a data structure within each level of traversal then in that case there exists one dependency factor which is related to the Inorder, Preorder, or Postorder traversal technique.

This dependency is not at all recommended thus it is made sure that in order to avoid this level order tree is segregated when compared with other traversal techniques including preorder, inorder, or postorder traversal technique.

Since it is not possible to run this level order tree with respect to recursive algorithms then it gels well with breadth-first search techniques thus can be used with those traversal algorithms efficiently.

#### # Non-Recursive Tree Traversal Algorithm

Non-Recursive Traversal Algorithm for traversing again deals with mostly stack where the elements present in the node in form of information again needs inorder, preorder and postorder traversal but in different pattern again not including Low-level tree traversal technique.

Sometimes it is assumed that the flat traversal function will always solve the problem related to traversal as it consumes less space for stack but it is not necessary that it will happen so that the implementor will be using the flat version always.

At times while fetching and traversing within the trees and nodes the overhead arise can be more than expected then in that case it will always find some external pointer to make the functionality aligned for an expected manner.

Pre-order, inorder and postorder traversal techniques also get associated with the Non-recursive tree traversal technique which includes stack push and pops with setting root as vertex and simultaneously making the left subtree and right subtree traversal occurring with the respective scenario making the root of the vertex at the down or edge of the stack.

Let’s take a scenario where the inorder traversal is taking place than in that case the stack is pushed with zero initially and then the root is set as vertex following the same procedure until the stack is completely empty, then after that, the traversal moves to left subtree with vertex making as root and then the left subtree followed by right subtree.

In a similar format, both the pre-order and postorder traversal takes place in non-recursive tree traversal with respect to stack.

### Conclusion

Tree traversal techniques play a pivotal role in solving any of the graph theory-related scenarios where the traversal happens with help of binary trees. Tree as a data structure provides a lot of flexibility in order to get the information stored at a node in each level from root to the depth of the entire tree.

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