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  1. Binary Trees Chapter 6

  2. Linked Lists Suck • By now you realize that the title to this slide is true… • When we are talking about searching or representing data structures that need a hierarchical structures. • We need a better structure… • So we get binary trees

  3. Tree definition • Here is a (recursive, of course) definition for a tree: • An empty structure is an empty tree • If t1,…,tk are disjointed trees, then the structure whose root has as its children the roots of t1,…,tk is also a tree • Only structures generate by rules 1 and 2 are trees.

  4. More terminology • Each node has to be reachable from the roots through a unique sequence of arcs called a path. • The number of arcs in a path is called the length of the path. • The level of a node is the length of the path from the root to the node plus 1. • The height of a non-empty tree is the maximum level of a node in the tree.

  5. Special Trees • An empty tree has a height of zero. • A single node tree is a tree of height 1. • This is the only case where a node is both a root and a leaf.

  6. Binary Trees • According to the definition of trees, a node can have any number of children. • A binary tree is restricted to only having 0, 1, or 2 children. • A complete binary tree is one where all the levels are full with exception to the last level and it is filled from left to right. • A full binary tree is one where if a node has a child, then it has two children.

  7. Full Binary Tree Theorem • For all the nonempty binary trees whose nonterminal node have exactly two nonempty children, the number of leaves m is greater than the number of nonterminal node k and m = k + 1.

  8. Binary Search Trees • A binary search tree (BST) is a binary tree that has the following property: For each node n of the tree, all values stored in its left subtree are less than value v stored in n, and all values stored in the right subtree are greater than v. • This definition excludes the case of duplicates. They can be include and would be put in the right subtree.

  9. Binary Tree Traversals • A traversal is where each node in a tree is visited and visited once • For a tree of n nodes there are n! traversals • Of course most of those are hard to program • There are two very common traversals • Breadth First • Depth First

  10. Breadth First • In a breadth first traversal all of the nodes on a given level are visited and then all of the nodes on the next level are visited. • Usually in a left to right fashion • This is implemented with a queue

  11. Depth First • In a depth first traversal all the nodes on a branch are visited before any others are visited • There are three common depth first traversals • Inorder • Preorder • Postorder • Each type has its use and specific application

  12. Insertion • In order to build a tree you must be able to insert into the tree • In order to do this you need to know where the nodes goes • Typically the tree is searched looking for a null pointer to hang the new element from • There are two common ways to do this • Use a look ahead or check for null as the first line in the code

  13. More insertion • I prefer to check for null as the first thing I do in my code • It simplifies some of the tests • And makes for a really easy to check for base case

  14. Code InsertionHelper( Node *n, T data ) { if ( node == 0 ) return new Node( data ); if ( n->getData() < data ) setLeft( InsertionHelper( n->getLeft(), data); else setRight( InsertionHelper( n->getRight(), data); }

  15. Deletion • Deletion poses a bigger problem • When we delete we normally have two choices • Deletion by merging • Deletion by copying

  16. Deletion by Merging • Deletion by merging takes two subtrees and merges them together into one tree • The idea is you have a node n to delete • N can have two children • So you find the smallest element in n’s left subtree • You then take n’s right subtree and merge it to the bottom of the left subtree • The root of the left subtree replaces n

  17. Deletion by copying • This will simply swap values and reduce a difficult case to an easier one • If the node n to be deleted has no children, • easy blow it away • If it has one child • Easy simply pass n’s child pointer up, make n’s parent point to n’s child and blow n away • If n has two child, • Now we have deletion by copying

  18. Details • We find the smallest value in n’s right subtree • We will take the value from that node and put it in place of the value in n • We will then blow away the node that had the smallest value in it