Chapter 18
Binary Trees
Data Structures and Design in Java © Rick Mercer
A 3rd way to structure data
w We have considered two data structures — Arrays — Singly linked
w Both are linear — each node has one successor and one predecessor
(except the first and last)
w We now consider a hierarchical data structure where each node may have two successors, each of which which may have two successors, and so on
Trees in General
w A tree has a set of nodes and directed edges that connect them
w One node is distinguished as the root w Every node (except the root) is connected by
exactly one edge from exactly one other node w A unique path traverses from the root to each
node Root
Some tree terminology
Node An element in the tree references to data and other nodes Path The nodes visited as you travel from root down Root The node at the top It is upside down! Parent The node directly above another node (except root) Child The node(s) below a given node Size The number of descendants plus one for the node itself Leaves Nodes with no children Height The length of a path (number of edges, -1 for empty trees) Levels The top level is 0, increases Degree The maximum number of children from one node
Trees used in many ways w Hierarchical files systems
— In Windows, \ represents an edge (or / in Linux) — Each directory may be empty, have children, some
of which may be other directories • A tiny bit of MAC's file system
. . . . . . . . . . . .
/
bin
test sleep
etc .Spotlight-V100
sync nanorc mail.rc
A few uses of trees w Implement data base systems w Store XML data from a web service as a Java
collection DOM tree w Binary Search Trees insert, remove, and find are O(log n)
56 / \ 25 80 / \ \ 12 30 107
w Compilers: Expression tree, Symbol tree w Store game of 20?s w Once used as our logo
The Binary Tree
w Structure has nodes to store an element along with the left and right children (binary trees)
— root is like first, front, or top in a singly linked structure
"T"
"R" "L"
root
edges
nodes
Binary Trees
w A binary tree is a tree where all nodes have zero, one or two children
w Each node is a leaf (no children), has a right child, has a left child, or has both a left and right child
A
C B
F D
D
Application: Huffman Tree
w Binary trees were used in a famous first file compression algorithm Huffman Tree
w Each character is stored in a leaf w Follow the paths
— 0 go left, 1 go right — a is 01, e is 11 — What is t?
— What is 0001101100100010000001000111111
— 31 bits vs. 12*8 = 96 bits
'a'
't'
' ' 'e'
'h' 'r'
Application: Binary Search Trees
w Insert, Search, Remove Operations:O(log n)
50
75 25
12 35
28 41
66 90
81 95 54
Binary Search Trees
w A Binary Search Tree (BST) data structure is a binary tree with an ordering property
w BSTs are used to maintain order and faster retrieval, insertion, and removal of individual elements
w A Binary Search Tree (BST) is — an empty tree — consists of a node called the root, and two children, left
and right, each of which are themselves binary search trees. Each BST contains a key at the root that is greater than all keys in the left BST while also being less than all keys in the right BST. Key fields are unique, duplicates not allowed.
Are these BSTs? only the keys are shown
50
75 25
12 45 66 90
50
75 25
12 55 73 90
Is this a BST?
Is this a BST?
BST Algorithms only the keys are shown w Think about an algorithm that retrieves a node
— It is similar to binary search. w Start at root, go left or right until?_____
(Ex: target == 65)
w Assuming the BST is "balanced", what is the tightest upper bound in Big-O?
50
75 25
12 36 65 90
values must be unique
w insert returns false if the key exists — In this case, the mapping is not added to the
collection
insert only the keys are shown
w Start at the root and go either left or right to find the insertion point.
50
75 25
12 36 65 90
tracker root
To insert 28: 28 < 50 so go left; 28 > 25 so go right; left link from 36 is null so stop
Adding the new node
w Replace the left child of 36, which was null, with a new BST node with data == 28
50
75 25
12 36 65 90
28
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