9780324782011_PPT_ch09.ppt

Data Structures Using C++ 2E
Chapter 9
Searching and Hashing Algorithms

Data Structures Using C++ 2E 2
Objectives
• Learn the various search algorithms
• Explore how to implement the sequential and binary
search algorithms
• Discover how the sequential and binary search
algorithms perform
• Become aware of the lower bound on comparison-
based search algorithms
• Learn about hashing

Search Algorithms
• Item key
– Unique member of the item
– Used in searching, sorting, insertion, deletion
• Number of key comparisons
– Comparing the key of the search item with the key of
an item in the list
• Can use class arrayListType (Chapter 3)
– Implements a list and basic operations in an array

Sequential Search
• Array-based lists
– Covered in Chapter 3
• Linked lists
– Covered in Chapter 5
• Works the same for array-based lists and linked lists
• See code on page 499

Sequential Search Analysis
• Examine effect of for loop in code on page 499
• Different programmers might implement same
algorithm differently
• Computer speed affects performance

Sequential Search Analysis (cont’d.)
• Sequential search algorithm performance
– Examine worst case and average case
– Count number of key comparisons
• Unsuccessful search
– Search item not in list
– Make n comparisons
• Conducting algorithm performance analysis
– Best case: make one key comparison
– Worst case: algorithm makes n comparisons

• Determining the average number of comparisons
– Consider all possible cases
– Find number of comparisons for each case
– Add number of comparisons, divide by number of
cases

• Determining the average number of comparisons
(cont’d.)

Ordered Lists
• Elements ordered according to some criteria
– Usually ascending order
• Operations
– Same as those on an unordered list
• Determining if list is empty or full, determining list
length, printing the list, clearing the list
• Defining ordered list as an abstract data type (ADT)
– Use inheritance to derive the class to implement the
ordered lists from class arrayListType
– Define two classes

Ordered Lists (cont’d.)

Binary Search
• Performed only on ordered lists
• Uses divide-and-conquer technique
FIGURE 9-1 List of length 12
FIGURE 9-2 Search list, list[0]...list[11]
FIGURE 9-3 Search list, list[6]...list[11]

Binary Search (cont’d.)
• C++ function implementing binary search algorithm

• Example 9-1
FIGURE 9-4 Sorted list for a binary search
TABLE 9-1 Values of first, last, and mid and the
number of comparisons for search item 89

Insertion into an Ordered List
• After insertion: resulting list must be ordered
– Find place in the list to insert item
• Use algorithm similar to binary search algorithm
– Slide list elements one array position down to make
room for the item to be inserted
– Insert the item
• Use function insertAt (class arrayListType)

Insertion into an Ordered List (cont’d.)
• Algorithm to insert the item
• Function insertOrd implements algorithm

• Add binary search algorithm and the insertOrd
algorithm to the class orderedArrayListType

• class orderedArrayListType
– Derived from class arrayListType
– List elements of orderedArrayListType
• Ordered
• Must override functions insertAt and insertEnd
of class arrayListType in class
orderedArrayListType
– If these functions are used by an object of type
orderedArrayListType, list elements will remain
in order

• Can also override function seqSearch
– Perform sequential search on an ordered list
• Takes into account that elements are ordered
TABLE 9-4 Number of comparisons for a list of length n

Lower Bound on Comparison-Based
Search Algorithms
• Comparison-based search algorithms
– Search list by comparing target element with list
elements
• Sequential search: order n
• Binary search: order log2n

Lower Bound on Comparison-Based
Search Algorithms (cont’d.)
• Devising a search algorithm with order less than
log2n
– Obtain lower bound on number of comparisons
• Cannot be comparison based

Hashing
• Algorithm of order one (on average)
• Requires data to be specially organized
– Hash table
• Helps organize data
• Stored in an array
• Denoted by HT
– Hash function
• Arithmetic function denoted by h
• Applied to key X
• Compute h(X): read as h of X
• h(X) gives address of the item

Hashing (cont’d.)
• Organizing data in the hash table
– Store data within the hash table (array)
– Store data in linked lists
• Hash table HT divided into b buckets
– HT[0], HT[1], . . ., HT[b – 1]
– Each bucket capable of holding r items
– Follows that br = m, where m is the size of HT
– Generally r = 1
• Each bucket can hold one item
• The hash function h maps key X onto an integer t
– h(X) = t, such that 0 <= h(X) <= b – 1

Hashing (cont’d.)
• See Examples 9-2 and 9-3
• Synonym
– Occurs if h(X1) = h(X2)
• Given two keys X1 and X2, such that X1 ≠ X2
• Overflow
– Occurs if bucket t full
• Collision
– Occurs if h(X1) = h(X2)
• Given X1 and X2 nonidentical keys

Hashing (cont’d.)
• Overflow and collision occur at same time
– If r = 1 (bucket size = one)
• Choosing a hash function
– Main objectives
• Choose an easy to compute hash function
• Minimize number of collisions
• If HTSize denotes the size of hash table (array size
holding the hash table)
– Assume bucket size = one
• Each bucket can hold one item
• Overflow and collision occur simultaneously

Hash Functions: Some Examples
• Mid-square
• Folding
• Division (modular arithmetic)
– In C++
• h(X) = iX % HTSize;
– C++ function

Collision Resolution
• Desirable to minimize number of collisions
– Collisions unavoidable in reality
• Hash function always maps a larger domain onto a
smaller range
• Collision resolution technique categories
– Open addressing (closed hashing)
• Data stored within the hash table
– Chaining (open hashing)
• Data organized in linked lists
• Hash table: array of pointers to the linked lists

Collision Resolution: Open Addressing
• Data stored within the hash table
– For each key X, h(X) gives index in the array
• Where item with key X likely to be stored

Linear Probing
• Starting at location t
– Search array sequentially to find next available slot
• Assume circular array
– If lower portion of array full
• Can continue search in top portion of array using mod
operator
– Starting at t, check array locations using probe
sequence
• t, (t + 1) % HTSize, (t + 2) % HTSize, . . ., (t + j) %
HTSize

Linear Probing (cont’d.)
• The next array slot is given by
– (h(X) + j) % HTSize where j is the jth probe
• See Example 9-4
• C++ code implementing linear programming

• Causes clustering
– More and more new keys would likely be hashed to
the array slots already occupied
FIGURE 9-7 Hash table of size 20 with certain positions occupied
FIGURE 9-6 Hash table of size 20 with certain positions occupied
FIGURE 9-5 Hash table of size 20

• Improving linear probing
– Skip array positions by fixed constant (c) instead of
one
– New hash address:
• If c = 2 and h(X) = 2k (h(X) even)
– Only even-numbered array positions visited
• If c = 2 and h(X) = 2k + 1, ( h(X) odd)
– Only odd-numbered array positions visited
• To visit all the array positions
– Constant c must be relatively prime to HTSize

Random Probing
• Uses random number generator to find next
available slot
– ith slot in probe sequence: (h(X) + ri) % HTSize
• Where ri is the ith value in a random permutation of the
numbers 1 to HTSize – 1
– All insertions, searches use same random numbers
sequence
• See Example 9-5

Rehashing
• If collision occurs with hash function h
– Use a series of hash functions: h1, h2, . . ., hs
– If collision occurs at h(X)
• Array slots hi(X), 1 <= hi(X) <= s examined

Quadratic Probing
• Suppose
– Item with key X hashed at t (h(X) = t and 0 <= t <=
HTSize – 1)
– Position t already occupied
• Starting at position t
– Linearly search array at locations (t + 1)% HTSize, (t
+ 22 ) % HTSize = (t + 4) %HTSize, (t + 32) % HTSize
= (t + 9) % HTSize, . . ., (t + i2) % HTSize
• Probe sequence: t, (t + 1) % HTSize (t + 22 ) %
HTSize, (t + 32) % HTSize, . . ., (t + i2) % HTSize

Quadratic Probing (cont’d.)
• See Example 9-6
• Reduces primary clustering
• Does not probe all positions in the table
– Probes about half the table before repeating probe
sequence
• When HTSize is a prime
– Considerable number of probes
• Assume full table
• Stop insertion (and search)

• Generating the probe sequence

• Consider probe sequence
– t, t +1, t + 22, t + 32, . . . , (t + i2) % HTSize
– C++ code computes ith probe
• (t + i2) % HTSize

• Pseudocode implementing quadratic probing

• Random, quadratic probings eliminate primary
clustering
• Secondary clustering
– Random, quadratic probing functions of home
positions
• Not original key

• Secondary clustering (cont’d.)
– If two nonidentical keys (X1 and X2) hashed to same
home position (h(X1) = h(X2))
• Same probe sequence followed for both keys
– If hash function causes a cluster at a particular home
position
• Cluster remains under these probings

• Solve secondary clustering with double hashing
– Use linear probing
• Increment value: function of key
– If collision occurs at h(X)
• Probe sequence generation
• See Examples 9-7 and 9-8

Deletion: Open Addressing
• Designing a class as an ADT
– Implement hashing using quadratic probing
• Use two arrays
– One stores the data
– One uses indexStatusList as described in the
previous section
• Indicates whether a position in hash table free,
occupied, used previously
• See code on pages 521 and 522
– Class template implementing hashing as an ADT
– Definition of function insert

Collision Resolution: Chaining (Open
Hashing)
• Hash table HT: array of pointers
– For each j, where 0 <= j <= HTsize -1
• HT[j] is a pointer to a linked list
• Hash table size (HTSize): less than or equal to the
number of items
FIGURE 9-10 Linked hash table

Collision Resolution: Chaining (cont’d.)
• Item insertion and collision
– For each key X (in the item)
• First find h(X) – t, where 0 <= t <= HTSize – 1
• Item with this key inserted in linked list pointed to by
HT[t]
– For nonidentical keys X1 and X2
• If h(X1) = h(X2)
– Items with keys X1 and X2 inserted in same linked list
• Collision handled quickly, effectively

• Search
– Determine whether item R with key X is in the hash
table
• First calculate h(X)
– Example: h(X) = T
• Linked list pointed to by HT[t] searched sequentially
• Deletion
– Delete item R from the hash table
• Search hash table to find where in a linked list R exists
• Adjust pointers at appropriate locations
• Deallocate memory occupied by R

• Overflow
– No longer a concern
• Data stored in linked lists
• Memory space to store data allocated dynamically
– Hash table size
• No longer needs to be greater than number of items
– Hash table less than the number of items
• Some linked lists contain more than one item
• Good hash function has average linked list length still
small (search is efficient)

• Advantages of chaining
– Item insertion and deletion: straightforward
– Efficient hash function
• Few keys hashed to same home position
• Short linked list (on average)
– Shorter search length
• If item size is large
– Saves a considerable amount of space

• Disadvantage of chaining
– Small item size wastes space
• Example: 1000 items each requires one word of
storage
– Chaining
• Requires 3000 words of storage
– Quadratic probing
• If hash table size twice number of items: 2000 words
• If table size three times number of items
– Keys reasonably spread out
– Results in fewer collisions

Hashing Analysis
• Load factor
– Parameter α
TABLE 9-5 Number of comparisons in hashing

Summary
• Sequential search
– Order n
• Ordered lists
– Elements ordered according to some criteria
• Binary search
– Order log2n
• Hashing
– Data organized using a hash table
– Apply hash function to determine if item with a key is
in the table
– Two ways to organize data

Summary (cont’d.)
• Hash functions
– Mid-square
– Folding
– Division (modular arithmetic)
• Collision resolution technique categories
– Open addressing (closed hashing)
– Chaining (open hashing)
• Search analysis
– Review number of key comparisons
– Worst case, best case, average case

9780324782011_PPT_ch09.ppt

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