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# Advanced Trees Part I - PowerPoint PPT Presentation

Advanced Trees Part I. Briana B. Morrison Adapted from Alan Eugenio & William J. Collins. Topics. Part I General Trees Multi-way Search Trees 2-3 Search Trees (and B trees) Tries* Part II AVL Trees Part III 2-3-4 Search Trees Red Black Trees. General Trees.

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Presentation Transcript

Briana B. Morrison

William J. Collins

Topics
• Part I
• General Trees
• Multi-way Search Trees
• 2-3 Search Trees (and B trees)
• Tries*
• Part II
• AVL Trees
• Part III
• 2-3-4 Search Trees
• Red Black Trees

General Trees
• Implementations of Ordered Trees
• Correspondence with binary trees

Rotated Form

Multiway Search Trees
• An m-way search tree is a tree in which, for some integer m called the order of the tree, each node has at most m children.
• If k <= m is the number of children, then all the nodes contain exactly k-1 keys, which partition all the keys into k subsets consisting of all the keys less than the first key in the node, all the keys between a pair of keys in the node, and all keys greater than the largest key in the node.

Example Multiway Search Tree

Balanced Multiway Trees (B-Trees)
• A B-tree of order m is an m-way search tree in which
• All leaves are on the same level.
• All internal nodes except the root have at most m non-empty children, and at least ceil(m/2) non-empty children.
• The number of keys in each internal node is one less than the number of its non-empty children, and these keys partition the keys in the children in the fashion of a search tree.
• The root has at most m children, but may have as few as 2 if it is not a leaf, or none if the tree consists of the root alone.

Example B tree

Why Use B-Trees?
• Remember that performance is related to the height of the tree
• We want to minimize the height of the tree
• Used to process external records (information too large to put into memory), minimizes number of accesses to secondary peripheral

2-3 Search Trees
• B-Tree of degree 3
• Balanced search tree
• Either the tree is empty or T has two children:
• root contains 1 data item
• r is greater than each value in left subtree
• r is less than each value in right subtree
• T is of the form: root, left subtree, middle subtree, right subtree
• r has two data items
• smaller value in r is greater than everything in left subtree and smaller than everything in middle subtree
• larger value in r is greater than everything in middle subtree and small than everything in right subtree

Example

50, 90

20 70 120, 150

10 30, 40 60 80 100, 110 130,140 160

Nodes with 2 children must have 1 item

Nodes with 3 children must have 2 items

Leaves may contain 1 or 2 items

Traversal

50, 90

20 70 120, 150

10 30, 40 60 80 100, 110 130, 140 160

Inorder traversal is same as BST,

But have to also take care of two value nodes and middle trees.

Retrieval

50, 90

20 70 120, 150

10 30, 40 60 80 100, 110 130, 140 160

Find the value 80

Find the value 140

Find the value 95

Insertion into 2-3 Search Tree
• Find where it would belong
• If leaf has 1 value, add as second value
• If leaf has 2 values:
• split into leaves
• give parent middle value
• if parent now has 3 values,
• split, and move middle value up

Now insert 60…

60 Inserted:

Insert 30…

Now insert 100…

Now insert 50…

50 Inserted

Now insert 70…

Now insert 90…

90 Inserted

Deletion from a 2-3 Tree
• Locate
• If internal node, find successor and swap
• if leaf contains more than 1 value, delete
• else if left has 1 value, but sibling contains more than 1 value, redistribute values
• if no sibling has 2 values, merge and bring down value and recurse

Delete 70…

Now delete 100…

Now delete 80…

Tries – Lexicographical Search Tree
• Name comes from middle letters of word “retrieval”, rhymes with “pie”
• Represents strings of any type such as characters
• Each path from root to a leaf is one word
• To avoid confusion, can use endmarker symbol \$

Preprocessing the pattern speeds up pattern matching queries

After preprocessing the pattern, KMP’s algorithm performs pattern matching in time proportional to the text size

If the text is large, immutable and searched for often (e.g., works by Shakespeare), we may want to preprocess the text instead of the pattern

A trie is a compact data structure for representing a set of strings, such as all the words in a text

A tries supports pattern matching queries in time proportional to the pattern size

Preprocessing Strings

Definition of Trie
• A trie of order m is either empty or consists of an ordered sequence of exactly m tries of order m.

S

T

I

H

I

E

I

N

N

\$

S

N

N

\$

G

\$

\$

\$

\$

\$

Standard Trie (1)
• The standard trie for a set of strings S is an ordered tree such that:
• Each node but the root is labeled with a character
• The children of a node are alphabetically ordered
• The paths from the external nodes to the root yield the strings of S
• Example: standard trie for the set of strings

S = { bear, bell, bid, bull, buy, sell, stock, stop }

Standard Trie (2)
• A standard trie uses O(n) space and supports searches, insertions and deletions in time O(dm), where:

n total size of the strings in S

m size of the string parameter of the operation

d size of the alphabet

Word Matching with a Trie
• We insert the words of the text into a trie
• Each leaf stores the occurrences of the associated word in the text

Compressed Trie
• A compressed trie has internal nodes of degree at least two
• It is obtained from standard trie by compressing chains of “redundant” nodes

Suffix Trie (1)
• The suffix trie of a string X is the compressed trie of all the suffixes of X

a

d

e

b

c

Encoding Trie (1)
• A code is a mapping of each character of an alphabet to a binary code-word
• A prefix code is a binary code such that no code-word is the prefix of another code-word
• An encoding trie represents a prefix code
• Each leaf stores a character
• The code word of a character is given by the path from the root to the leaf storing the character (0 for a left child and 1 for a right child

c

a

d

b

b

r

a

c

r

d

Encoding Trie (2)
• Given a text string X, we want to find a prefix code for the characters of X that yields a small encoding for X
• Frequent characters should have long code-words
• Rare characters should have short code-words
• Example
• T1 encodes X into 29 bits
• T2 encodes X into 24 bits

T1

T2

Huffman’s Algorithm

AlgorithmHuffmanEncoding(X)

Inputstring X of size n

Outputoptimal encoding trie for X

C distinctCharacters(X)

computeFrequencies(C, X)

Qnew empty heap

for all c  C

Tnew single-node tree storing c

Q.insert(getFrequency(c), T)

while Q.size()> 1

f1 Q.minKey()

T1 Q.removeMin()

f2 Q.minKey()

T2 Q.removeMin()

Tjoin(T1, T2)

Q.insert(f1+ f2, T)

return Q.removeMin()

• Given a string X, Huffman’s algorithm construct a prefix code the minimizes the size of the encoding of X
• It runs in timeO(n + d log d), where n is the size of X and d is the number of distinct characters of X
• A heap-based priority queue is used as an auxiliary structure

2

4

a

c

d

b

r

5

6

a

b

c

d

r

2

4

5

2

1

1

2

11

a

c

d

b

r

6

a

5

2

2

4

a

b

c

d

r

c

d

b

r

5

2

2

Example

Frequencies

C++ Trie Declarations
• Every record has a Key that is an alphanumeric string.

class Trie {

public: …

private:

Trie_node *root;

};

struct Trie_node {

Record *data;

Trie_node *branch[num_chars];

// or vector

};