Data structure tree - intermediate

Data structure tree - intermediate

• 1.
  DATA STRUCTURE TREE
• 2.
  DATA STRUCTURE -TREE  Many applications are hierarchical in nature.  Linear data structures are not appropriate for these type of applications. Tree  2 President Vice President Executive Executive Vice President Executive Vice President Executive Executive Mashrafi Khan Mairufa Khan Junayed Ahmed Masrufa Ahmed Fahim Khan Ashfar Khan Moin Khan Imran Khan Bushra Khan Faizul Khan Mahboob Khan Mashrufa Khan Mashraba Khan Hierarchical structure of a company Genealogy tree of a family
• 3.
  COMMON USE OFTREE AS A DATA STRUCTURE  Representing hierarchical data  Storing data in a way that makes it easily searchable  Representing sorted lists of data  As a workflow for compositing digital images for visual effects  Routing algorithms Tree  3
• 4.
  TREE - DEFINITION As a data structure, a tree consists of one or more nodes, where each node has a value and a list of references to other (its children) nodes.  A tree must have a node designated as root. If a tree has only one node, that node is the root node. Root is never referenced by any other node.  Having more than one node indicates that the root have some (at least one) references to its children nodes and the children nodes (might) have references to their children nodes and so on. Tree  4 A B C D E F JIHG K ML ON ROOT VALUE CHILDCHILD
• 5.
  TREE - DEFINITION Generally it is considered that in a tree, there is only one path going from one node to another node.  There cannot be any cycle or loop.  The link from a node to other node is called an edge.  An arrowed edge indicates flow from P to Q.  An straight line edge indicates flow from P to Q and Q to P. Tree  5 A B C D E F JIHG K ML ON If node F is reached through node B, than the link from node K to node F will not be considered. If link from node L to node C is considered, than there will be a cycle among nodes C, G, and L. P Q P Q A straight line is generally used to represent the links between the nodes of a tree.
• 6.
  TREE - DEFINITION Nodes  Parent Nodes & Child Nodes  Leaf Nodes: nodes with no child  Root Node: node with no parent  Sub Tree: the tree rooted by a child  Level of a tree:  Root at level 0;  Each children have the level one more than its parent.  Height/depth of the tree: Total number of Levels  Height of a node: Total number of levels from bottom [Tree height – node level]. Tree  6 A B C D E F JIHG K ML ON LEVEL 0 1 2 3 Height of this tree is 4, as there are four levels (0…3). Height of root A is 4; Height of nodes B, C, D is 3; Height of E, F, G, H, I, J is 2; Height of nodes K, L , M, N, O is 1.
• 7.
  m-ARY TREE  ATree is an m-ary Tree when each of its node has no more than m children. Tree  7 A B C D E F JIHG K ML ON A B D E F IH GK ML ON 2-ary tree 3-ary tree
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  BINARY TREE (BT) Each node of a binary Tree has at most 2 children. Tree  8 A B D E F IH GK ML ON Binary tree
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  COMPLETE AND FULLBINARY TREE  A complete/full binary tree is a binary tree, which is completely filled with nodes from top to bottom and left to right.  The tree starts from the root (top), goes to the next level with first the left child and then the right child (left to right). The process repeats for each next level with each node till the last (bottom) level.  In complete binary tree some of the nodes only for the bottom level might be absent (here nodes after N).  In Full binary tree the last/bottom level must also be filled up. Tree  9 A B D E F IH GK ML N C J P
• 10.
  COMPLETE AND FULLBINARY TREE  A full binary tree of depth n is a binary tree of depth n with 2n - 1 nodes, n >=0.  A binary tree with k nodes and depth n is a complete binary tree if and only if its nodes correspond to the nodes numbered from 0 to k-1 in the full binary tree of depth n. Tree  10 A B D E F IH GK ML N C J P Level=0, # of nodes=20=1 Level=1, # of nodes=21=2 Level=2, # of nodes=22=4 Level=3, # of nodes=23=8 Total Number of Nodes: 20+ 21+ 22+ 23 = 24 - 1 = 15 Height of the tree: Log215 = 4 If total number of nodes are n, Nodes at each level L = 2L Bottom Level = BL Height h = BL+1 = Log2n Total nodes = 2h – 1 = n
• 11.
  TRAVERSAL  Systematic wayof visiting all the nodes.  Methods:  Inorder  Postorder  Preorder  They all traverse the left subtree before the right subtree. Tree  11
• 12.
  BINARY SEARCH TREE(BST)  Is a Binary Tree such that:  Every node entry has a unique key (i.e. no duplication item).  All the keys in the left subtree of a node are less than the key of the node.  All the keys in the right subtree of a node are greater than the key of the node. Tree  12 43 31 64 20 40 8956 3328 47 59 Fred Dan Mary Alan Eve SueKate EricBill Greg Len Integer Key String Key
• 13.
  BST - INSERT Tree 13 43 31 64 20 40 8956 3328 47 59 > < < < <>< > > > Fred Dan Mary Alan Eve SueKate EricBill Greg Len > < < > > << > > < Integer Key String Key 43 31 64 40 20 89 56 47 33 28 59Fred Mary Kate Dan Len Alan Eve Bill Sue Greg Eric
• 14.
  BST - SEARCH Search Elements 59 and 42 Fahad Ahmed CSC 2015: Data Structures Tree  14 43 31 64 20 40 8956 3328 47 59 43 < 59 64 > 59 56 < 59 59 = 59 43 > 42 31 < 42 40 < 42 ? 42
• 15.
  BST - DELETE Delete 47 (leaf node);  Delete 40 (have only one child);  Delete 64 and 31 (have both child). Tree  15 43 31 64 20 40 8956 3828 47 59 32 33 5932 Leaf Node: Just DeleteLeaf Node: Just Delete Node with one child: connect the parent to the child and Delete Node with one child: connect the parent to the child and Delete Node with two children on right subtree: Replace with the child with highest value either from left or right subtree Node with two children on left subtree: Replace with the child with lowest value either from left or right subtree