In the introduction we used the binary search algorithm to find data stored in an array. This method is very effective as each iteration reduced the number of items to search by one-half. However since data was stored in an array insertions and deletions were not efficient. Binary search trees store data in nodes that are linked in a tree-like fashion. For randomly inserted data search time is O(lg n). Worst-case behavior occurs when ordered data is inserted. In this case the search time is O(n). See Cormen  for a more detailed description.
A binary search tree is a tree where each node has a left and right child. Either child or both children may be missing. Figure 3-2 illustrates a binary search tree. Assuming k represents the value of a given node then a binary search tree also has the following property: all children to the left of the node have values smaller than k and all children to the right of the node have values larger than k. The top of a tree is known as the root and the exposed nodes at the bottom are known as leaves. In Figure 3-2 the root is node 20 and the leaves are nodes 4 16 37 and 43. The height of a tree is the length of the longest path from root to leaf. For this example the tree height is 2.
Figure 3-2: A Binary Search Tree
To search a tree for a given value we start at the root and work down. For example to search for 16 we first note that 16 < 20 and we traverse to the left child. The second comparison finds that 16 > 7 so we traverse to the right child. On the third comparison we succeed.
Each comparison results in reducing the number of items to inspect by one-half. In this respect the algorithm is similar to a binary search on an array. However this is true only if the tree is balanced. For example Figure 3-3 shows another tree containing the same values. While it is a binary search tree its behavior is more like that of a linked list with search time increasing proportional to the number of elements stored.
Figure 3-3: An Unbalanced Binary Search Tree
Let us examine insertions in a binary search tree to determine the conditions that can cause an unbalanced tree. To insert an 18 in the tree in Figure 3-2 we first search for that number. This causes us to arrive at node 16 with nowhere to go. Since 18 > 16 we simply add node 18 to the right child of node 16 (Figure 3-4).
Now we can see how an unbalanced tree can occur. If the data is presented in an ascending sequence each node will be added to the right of the previous node. This will create one long chain or linked list. However if data is presented for insertion in a random order then a more balanced tree is possible.
Deletions are similar but require that the binary search tree property be maintained. For example if node 20 in Figure 3-4 is removed it must be replaced by node 18 or node 37. Assuming we replace a node by its successor the resulting tree is shown in Figure 3-5. The rationale for this choice is as follows. The successor for node 20 must be chosen such that all nodes to the right are larger. Therefore we need to select the smallest valued node to the right of node 20. To make the selection chain once to the right (node 38) and then chain to the left until the last node is found (node 37). This is the successor for node 20.
Figure 3-4: Binary Tree After Adding Node 18
Figure 3-5: Binary Tree After Deleting Node 20
An ANSI-C implementation for a binary search tree is included.
and comparison operators
compEQ should be altered to reflect the data stored in the tree. Each Node
parent pointers designating
each child and the parent. The tree is based at
and is initially
insert allocates a new node and inserts it in the tree. Function
deletes and frees a node from the tree. Function
find searches the tree for a
The binary tree algorithm has been implemented as objects using a module for the algorithm and a class for the nodes. It has also been implemented as a class using arrays. The array implementation is recommended.