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Data structures project

  1. 1. CAP-204 (FUNDAMENTALS OF DATA STRUCTURES) TERM PAPER ON Role of Data Structures in Programming languagesSUBMITTED TO:Tajinder Mam SUBMITTED BY: Sachin Raj A34 G(A) RD3901
  2. 2. ACKNOWLEDEGEMENTI would like to acknowledge and extend my heartfelt gratitude to thefollowing persons who have made the completion of this Term Paperpossible:Our Chancellor, Mr. Ashok Mittal for his vital encouragement andsupport. Our Executive Dean, Mrs. Rashmi Mittal for her understanding andassistance.Tajinder Mam(Lecturer) for the help and inspiration he extended.Most especially to my family and friends for assisting in the collectionof the topics for the term paper.and to God, who made all things possible.
  3. 3. Table of ContentsData StructuresA data structure is an arrangement of data in a computers memory or even diskstorage. An example of several common data structures are arrays, linked lists,queues, stacks, binary trees, and hash tables. Algorithms, on the other hand, areused to manipulate the data contained in these data structures as in searchingand sorting.Many algorithms apply directly to a specific data structures. When working withcertain data structures you need to know how to insert new data, search for aspecified item, and deleting a specific item.Commonly used algorithms include are useful for:  Searching for a particular data item (or record).  Sorting the data. There are many ways to sort data. Simple sorting, Advanced sorting  Iterating through all the items in a data structure. (Visiting each item in turn so as to display it or perform some other action on these items) Characteristics of Data Structures Data Structure Advantages Disadvantages Quick inserts Slow search Array Fast access if index Slow deletes known< Fixed size Slow inserts Faster search than Ordered Array Slow deletes unsorted array Fixed size Slow access to Stack Last-in, first-out acces other items Slow access to Queue First-in, first-out access other items Linked List Quick inserts Slow search
  4. 4. Data Structure Advantages Disadvantages Quick deletes Quick search Quick inserts Deletion algorithm Binary Tree Quick deletes is complex (If the tree remains balanced) Quick search Quick inserts Complex to Red-Black Tree Quick deletes implement (Tree always remains balanced) Quick search Quick inserts Quick deletes Complex to 2-3-4 Tree (Tree always remains implement balanced) (Similar trees good for disk storage) Slow deletes Very fast access if key is Access slow if key Hash Table known is not known Quick inserts Inefficient memory usage Quick inserts Slow access to Heap Quick deletes other items Access to largest item Some algorithms Best models real-world Graph are slow and very situations complexData structure used in LanguageAssembly languages and some low-level languages, such as BCPL, generally lacksupport for data structures. Many high-level programming languages, on the
  5. 5. other hand, have special syntax or other built-in support for certain datastructures, such as vectors (one-dimensional arrays) in the C language, multi-dimensional arrays in Pascal, linked lists in Common Lisp, and hash tables in Perland in Python. Many languages also provide basic facilities such as references andthe definition record data types, that programmers can use to build arbitrarilycomplex structures.Most programming languages feature some sort of library mechanism that allowsdata structure implementations to be reused by different programs. Modernlanguages usually come with standard libraries that implement the most commondata structures. Examples are the C++ Standard Template Library, the JavaCollections Framework, and Microsofts .NET Framework.Modern languages also generally support modular programming, the separationbetween the interface of a library module and its implementation. Some provideopaque data types that allow clients to hide implementation details. Object-oriented programming languages, such as C++, .NET Framework and Java, useclasses for this purpose.With the advent of multi-core processors, many known data structures haveconcurrent versions that allow multiple computing threads to access the datastructure simultaneously. Data structure used in c++ and c languageIn c++ datastructure are used:Linear Data Structure  ARRAY
  6. 6.  LINKEDLIST  STACK  QUEUES  RECURSIONNon-linear Data Structure  TREES  GRAPHArrayLinear Arrays:A linear array is a list of a finite number n of homogeneous dataelements( Data elements of same type) such that:  The elements of the array are referenced respectively by an index set consisting of n consecutive numbers.  The elements of the array are stored respectively in successive memory location.The number n of elements is called the length or size of an array.Length=UB-LB+1UB=Upper BoundLB=Lower BoundExample:
  7. 7. A[5],A[10]A[1],A[2],A[3],A[4]---------------,A[10]Representation of linear ArraysA[0] 200 1A[1] 201 2A[2] 202 3A[3] 203 4A[4] 204 5A[5] 205 6A[6] 206 7A[7] 207 8LOC(LA[K]=Base(LA)+w(K-Lower bound)1) LOC(LA[K]=address of the element LA[K] of the array LA2) Base(LA)= Base Address of LA3) W=number of words per memory cell for the array LA.Operation on arrays  Traversing  Insertion  Deletion  Searching  SortingInserting in Linear Array
  8. 8. INSERT(LA, n, k, Item)1. [Initialize counter] Set j=n2. Repeat steps 3 and 4 while j>=k.3.[Move jth Element Downward] Set LA[j+1]=LA[j]4. [Decrease Counter ] Set j=j-1 [End of step 2 loop]5. [Insert Element] Set LA[k]=Item6. [Reset n] Set n=n+17. ExitDeleting from linear Array1. Set Item=LA[k]2.Repeat for j=k to n-1 [Move j+1st element upward] Set LA[j]=La[j+1] [End of loop]3. [Reset n] Set n=n-14. Exit Sorting of Array in Data StructureSorting:
  9. 9. Let A be a list of n numbers. Sorting A refers to the operation ofrearranging the elements of A so they are in increasing order, i.e.A[1]<A[2]<A[3]<……<A[n]EXAMPLE-8,4,19,2,7,13After Sorting,2,4,7,8,13,19Types of Sorting techniques:  Bubble sort  Selection Sort  Insertion Sort  Merge Sort  Quick Sort  Heap SortBubble Sort:Suppose the list of numbers A[1],A[2],…..,A[n] is in memory. Thebubble sort algorithm works as follows:Step 1: Compare A[1] and A[2] and arrange them in desired order, sothat A[1]<A[2], then compare A[2] and A[3]and arrange them suchthat A[2]<A[3] continue until we compare A[N-1] with A[N] andarrange them so that A[N-1]<A[N].
  10. 10. Note: Step 1 Involves n-1 comparisons. When step1 will completeA[N], will contain the largest element. Step 2: Repeat Step 1 with one less comparison; that is now we stopafter we compare and possibly rearrange A[N-2] and A[N-1].( Step 2involves n-2 Comparisons)Step N-1: Compare A[1] and A[2] and Arrange them so that A[1]<A[2].The process of sequentially traversing through all r part of a list isfrequently called a “Pass”.Example:30,55,20,82,63,19,13,57PASS1:1) Compare 30<55 No change.2) Compare 55<20(interchange) 30,20,55,82,63,19,13,573) Compare 55<82 no change.4) Compare 82<63 (interchange) 30,20,55,63,82,19,13,575) Compare 82<19(interchange) 30,20,55,63,19,82,13,576) Compare 82<13 (interchange) 30,20,55,63,19,13,82,577)Compare 82<57(interchange) 30,20,55,63,19,13,57,82At pass 1 82 reached it correct Nth position.( N-1 Comparison)30,20,55,63,19,13,57,82Pass 2:
  11. 11. 1) Compare 30<20( Interchange) 20,30,55,63,19,13,57,82 2) Compare 30<55 No change 3) Compare 55<63 no change 4) Compare 63<19 (interchange)30,20,55,19,63,13,57,82 5) Compare 63<13 (interchange) 30,20,55,19,13,63,57,82 6) Compare 63<57(interchange) 30,20,55,19,13,57,63,82 At pass2 2nd largest no reach N-1 position(N-2 Comparison)The same procedure will repeat till n-1 no of passes and after n-1 passthe data will be sorted.Selection sort:Suppose an array A with n elements A[1], A[2],….,A[N] is in memory.The selection sort algorithm first find the smallest element in the listand put it in the first position. Then find the second smallest elementin the list and put in the second position and so on.Pass1: find the location loc of the smallest in the list of N elements.A[1],A[2],…,A[N] and then interchange A[LOC] and A[1]. Then A[1] isSorted.Pass2: find the location LOC of the smallest in the sublist of N-1Elements. A[2],A[3]….A[N], and then interchange A[loc] and A[2].Pass N-1: find the loc of the smaller of the elements A[N-1],A[N] andthen interchange A[LOC] and A[N-1] then A[1], A[2]……A[N] is sortedExample:
  12. 12. Selection Sort for 8 elements77,33,44,11,88,22,66,55PASS A[1] A[2] A[3] A[4] A[5] A[6] A[7] A[8]K=1,LOC=4 77 33 44 11 88 22 66 55K=2, 11 33 44 77 88 22 66 55LOC=6K=3 11 22 44 77 88 33 66 55,LOC=6K=4 11 22 33 77 88 44 66 55,LOC=6K=5 11 22 33 44 88 77 66 55,LOC=8K=6, 11 22 33 44 55 77 66 88LOC=7
  13. 13. K=7, 11 22 33 44 55 66 77 88LOC=7 Insertion Sort:  Suppose an array A with n elements A[1],A[2],…..,A[N] is in memory. The insertion sort algorithm scans A from A[1] to A[N], inserting each element A[K] in to its proper position in the previously sorted sub array a[1],A[2],……,A[K-1] i.e.  Pass1 : A[1] by itself is trivially sorted.  Pass2 : A[2] is inserted either before or after A[1] so that: A[1], A[2] is sorted.  Pass 3 : A[3] is inserted into its proper place in A[1], A[2] that is before A[1], between A[1] and A[2], or after A[2]. So that A[1],A[2],A[2] is sorted.  Pass N: A[N] is inserted in to its proper place in a[1],A[2],……., A[N-1]Merge sort:
  14. 14. Merging(A,R,B,S,C)1.[Initialize] Set NA=1, NB=1 and ptr =12.[Compare] Repeat while NA<=R and NB<=Sif A[NA]<B[NB], thena) [Assign Element from A to C] Set c[PTR]=A[NA]b) [Update pointers] Set ptr=ptr+1 and NA=NA+1Elsea) [Assign Element from B to C] Set c[PTR]=B[NB]b) [Update pointers] Set ptr=ptr+1 and NB=NB+1[End of if Structure][End of loop]3. [Assign Remaining Elements to C]If NA>R thenRepeat for k=0,1,2,…..S-NB;C[PTR+k]=B[NB+k][End of Loop[]Else:Repeat for k=0,1,2,…..R-NA;C[PTR+k]=A[NA+k][End of Loop[]
  15. 15. [End of if structure]4. ExitMerge Sort Example66,33,40,22,55,88,60,11,80,20,50,44,77,30Each pass of the merge-sort algorithm will start at the begning of thearray A and merge pairs of sorted sub arrays as follows:Pass 1: Merge each pair of elements to obtain the following list ofsorted pairs.33,66 22,40 55,88 11,60 20,80 44,50 30,70Pass 2: Merge each pairs of pairs to obtain the following list of sortedelements.22,33,40,60 11,55,60,88 20,44,50,80 30,70Pass 3: Merge each pair of sorted elements to obtain the followingtwo sorted sub arrays.11,22,33,40,55,60,66,88 20,30,44,50,77,80Pass 4: Merge the two sorted list11,20,22,30,33,40,44,50,55,60,66,77,80,88 LINK LIST  List: List refers to a linear collection of data items.
  16. 16.  Link-List: A linked list or a one way list is a linear collection of data elements called nodes, Where the linear order is given by means of pointers. Each node in the list is divided in two parts: The first part contains the information and the second contains the address of the next node in the list called link field. The Linked List contains a list pointer variable called start pointer which contains the address of the first node in the list. The null pointer signals the end of the list. A special case is the list that has no nodes such a list is called the null list or empty list.Representation of Linked lists in memory. Start=4 So info[4]=M Link[4]=2 so info[2]= C Link[2]=8 so info[8]=A Link[8]=NULL INFO LINK
  17. 17. Operation on link-list:  Traversing  Insertion  Deletion  Searching  SortingTraversing a link-listAlgorithm:
  18. 18. Let LIST be a linked list in memory. This algorithm traverse LIST,applying an operation process to each element of LIST. The variablePTR points to the node currently being processed.1) Set PTR:=Start [Initializes pointer PTR]2) Repeat Steps 3 and 4 while PTR!=NULL.3) Apply Process to info[ptr].4) Set PTR=link[ptr] [now points to the next node] [End of step 2 loop]5) ExitSearching in Link List:1) Set PTR=START.2) Repeat Step 3 While PTR!=NULL3)if item = INFO[PTR] thenSet LOC=PTR; ElseSet PTR=Link[PTR].4) Set LOC=NULL5) EXITInsertion in to Link List:Three insertion can be done:
  19. 19.  Inserts a node at the beginning of the list.  inserts a node after the node with a given location.  insert a node in sorted listInsertion at the beginning of a list:INSFIRST(INFO,LINK,START,AVAIL,ITEM)1) [Overflow ] if Avail =Null, then write :overflow and Exit.2) [Remove first node from Avail list] Set New=Avail and Avail=Link[Avail].3)Set Info[New]=Item [Copies New data into new node]4)Set Link[New]=Start [New Node now points to original First Node]5)Set Start=New[Change Start so it points to the new node]6)Exit.Inserting into Sorted Link-List:FINDA(INFO,LINK,START,ITEM,LOC)1.[LIST empty] if Start=Null, then Set LOC=NULL and return.2.[Special Case] if Item<Info[Start], then Set LOC=Null and Return.3. Set Save=Start and PTR=Link[Start]4. Repeat Steps 5 and 6 while ptr!=null5. if item<info[ptr] then:set Loc=Save and return
  20. 20. [end of if]6. Set Save=Ptr and Ptr=link[Ptr] [end of step 4 loop]7. Set loc=Save.8.ReturnDeletion From a link list:Deleting a node with a given item of information:Find B( Info, Link, Start, Item, Loc, Locp ):This procedure finds the location LOC of the first node N whichcontains ITEM and the location LOCP of the node preceding N. if itemdoes not appear in the list then the procedure sets LOC=NULL and ifitem appears in the first node then it sets LOCP=NULL1. If Start=NULL thenSet LOC=NULL and LOCP=NULL and return2. [Item in First Node] If INFO[Start]=ITEM thenSet LOC=Start and LOCP=NULL and Return.3. Set Save=Start and Ptr=Link[Start]4. Repeat Steps 5 and 6 while Ptr!=Null5. If Info[PTR]=Item thenSet LOC=PTR and LOCP=Save6. Set Save=PTR and ptr=link[PTR] [End of Step 4 Loop]
  21. 21. 7. Set LOC=NULL8.ReturnHeader Link-List:A header link list is a link list which always contains a special node,called a header node at the beginning of the list. The following are thetwo kinds of widely used header lists:1.A grounded header list is a header list where the last node containsa NULL pointer.2.A Circular header list is a header list where the last node points backto the header Node. • Circular header lists are frequently used instead of ordinary link- lists because many operations are much easier to state and implement using header lists.1) The Null Pointer is not used and hence all pointers contain validaddresses.2) Every node has a predecessor, so the first node may not require aspecial case. StackA Stack is a list of elements in which an element may be inserted ordeleted only at one end called the top of the stack Lists( LIFO (Last In,First Out) )Basic operations of stack
  22. 22. “Push” is the term used to insert an element into a stack.“Pop” is the term used to delete an element from stack. etc.Array Implementation  Need to declare an array size ahead of time  Associated with each stack is TopOfStack for an empty stack, set TopOfStack to -1Push(1) Increment TopOfStack by 1.(2) Set Stack[TopOfStack] = XPop(1) Set return value to Stack[TopOfStack](2) Decrement TopOfStack by 1These operations are performed in very fast constant timePUSH(STACK, Top, MaxStk, Item)1.[Stack already filled]if top=MAXSTK then print: overflow and return2) Set TOP=TOP+1 [Increases top by 1]3) Stack[TOP]=Item.4) Return.
  23. 23. POP( Stack, Top, Item)1.[Stack Empty]if top=0 then print: Under flow and return2) ) Item =. Stack[TOP] [Assign top element to item]3) Set TOP=TOP-1 [Decreases top by 1]4) Return.Application of Stacks:Postponed DecisionsQuick Sort.Arithmetic Expressions(Polish Notation)Polish Notation: 1) For most common arithmetic operations, the operator symbol is placed between its two operands: Example: A+B,C-D 2) This is called infix Notation. With this notation, we must distinguish between (A+B)* C and A + (B*C). By using parantheses or some operator-precedence convention.
  24. 24. 3) Polish Notation: Refers to the notation in Which the operator symbol is placed before its two operands example: +AB,-CDQuick Sort:Quick sort is an algorithm of the divide and conquer type.that is theproblem of sorting a set is reduced to the problem of sorting twosmaller sets using two Stacks Lower and Upper. Example:44,33,11,55,77,90,40,60,99,22,88,661.Use the first element (44) . Beginning with last number 66 scan thelist from right to left comparing each number with 44 and stopping atthe first number less than 44. the number is 22 interchange 44 with22.22,33,11,55,77,90,40,60,99,44,88,662.Beginning with 22 next scan the list in the opposite direction fromleft to right comparing each number with 44 and stopping at the firstnumber greater than 44. the number is 55 interchange 55 with 44.22,33,11,44,77,90,40,60,99,55,88,66Recursion:Suppose P is a procedure containing either a call statement to itself ora call statement to a second procedure that may eventually result in acall statement back to the original procedure P then P is called aRecursive Procedure. A program will not continue to run indefinitely arecursive procedure must have the following two properties: 1) There must be certain criteria, called base criteria for which the procedure does not call itself.
  25. 25. 2) Each time the procedure does call itself, it must be closer to the base Criteria.A recursive procedure with these two properties is said to be welldefined. QueueLike a stack, a queue is also a list. However, with a queue, insertion isdone at one end, while deletion is performed at the other end.Accessing the elements of queues follows a First In, First Out (FIFO)order.Like customers standing in a check-out line in a store, the firstcustomer in is the first customer served.Basic operations:enqueue: insert an element at the rear of the listdequeue: delete the element at the front of the listQueue InsertThis procedure Inserts an element ITEM into a queue.[Queue Already Filled?]If FRONT=1 and REAR=N or if FRONT = REAR +1 then write overflow.2) [Find New Value of Rear]If FRONT=NULL then [QUEUE Initially Empty]SET FRONT=1 and REAR=1
  26. 26. ELSE if REAR=N thenSet REAR=1ELSE Set REAR=REAR+1[End of if]3) Set Queue[REAR]=Item.4) Return.There are several different algorithms to implement Enqueue andDequeueQueue Implementation of ArrayNaïve wayWhen enqueuing, the front index is always fixed and the rear indexmoves forward in the array.Dequeue  A deque is a linear list in which elements can be added or removed at either end but not in middle( Left ,Right pointer).  There are two variation of a Deque- namely an Input Restricted and an output-Restricted deque.  An input restricted deque is a deque which allows insertions at only one end of a list but allows deletions on both the ends of the list
  27. 27.  An output restricted Deque is a deque which allows deletions at only one end of the list but allows insertions at both the ends of the list. Tree  Arrays, linked lists, stacks and queues are used to represent linear and tabular data.  These structures are not suitable for representing hierarchical data.  In hierarchical data we have  ancestors,  descendants,  superiors,  subordinates, etcIntroduction to Trees  Fundamental data storage structures used in programming  Combine advantages of ordered arrays and linked lists  Searching can be made as fast as in ordered arrays  Insertion and deletion as fast as in linked listsTree characteristics
  28. 28.  Consists of nodes connected by edges  Nodes often represent entities such as people, car parts etc.  Edges between the nodes represent the way the nodes are related.  The only way to get from node to node is to follow a path along the edges.Tree Terminology  Root : node at the top of tree and without parent (A)  Internal node: node with at least one child (A, B, C, F)  External node: (leaf) node without children (E, I, J, K, G, H, D)  Ancestors of a node: parent, grandparent, grand-grandparent, etc  Height of a tree: maximum depth of any node (4)  Descendant of a node: child, grandchild, grand-grandchild, etc  Degree of an element: no. of children it has
  29. 29.  Subtree : tree consisting of a node and its descendants  Path: traversal from node to node along the edges that results in a sequenceBinary Trees  Every node in a binary tree can have at most two children.  The two children of each node are called the left child and right child corresponding to their positions.  A node can have only a left child or only a right child or it can have no children at all.Application on binary Tree  arithmetic expressions  decision processes  searchingComplete Binary Trees
  30. 30. It is that binary tree in which every level is fully occupied except, possibly, for the bottom level which is filled from left to right. complete B-Tree Not complete B-TreeBinary search tree(or ordered binary tree)It is a node-based binary tree data structure which has the followingproperties:  The left subtree of a node contains only nodes with keys less than the nodes key.  The right subtree of a node contains only nodes with keys greater than the nodes key.  Both the left and right subtrees must also be binary search trees.
  31. 31. Searching and Inserting in BSTSuppose an ITEM of information is given. The following algorithmfinds the location of ITEM in the binary search tree T, or inserts ITEMas a new node in its appropriate place in the tree.a) Compare ITEM with the root node N of the tree. (i) If ITEM<N, proceed to the left child of N (ii) If ITEM>N, proceed to right child of Nb) Repeat Step (a) until one of the following occurs:(i) We meet a node N such that ITEM=N. In this case the search is successful.(ii) We meet an empty subtree, which indicates that the search is unsuccessful and we insert. Graph
  32. 32. DefinitionA graph is a datastructure consisting of:_ a set of vertices (or nodes)._ a set of edges (or links) connecting the, G = (V, E) where V is a set of vertices, E = set of edges, and eachedge is formed from pair of distinct vertices in VIf we represent our problem data using a graph data structure, canuse standard graph algorithms (often available from code libraries) tosolve it.Graph AlgorithmsGraph algorithms that we will look at include:  Searching for a path between two nodes. Can be used in game playing, AI, route finding, ..  Finding shortest path between two nodes.  Finding a possible ordering of nodes given some constraints.Example:finding order of modules to take; order of actions to complete a task.A Graph ADT: OperationsNeed to define:  Operations for modifying and inspecting graph.  Data structure for graph itself.
  33. 33. For simple undirected, unlabelled graph, a small set of operations isenough, to:  Create a graph.  Add and remove edges to the graph.  Check if an edge exists.If we assume all nodes are indentified by a number, following C++functions can be used:graph(); // constructor˜graph(); // and destructor // (may be empty)void addedge(int n1, int n2);void removedge(int n1, int n2);logical edgeexists(int n1, int n2);A Simple Graph ADT using C++ ClassesUsing above representation we can have followingvery simple class definition:class graph(){
  34. 34. public:graph();˜graph();void addedge(int n1, int n2);void removedge(int n1, int n2);logical edgeexists(int n1, int n2);private:logical g[MaxSize][MaxSize];}This uses a fixed size array. Not ideal as may want graphs of varyingsize. May use pointers to allow variable sized arrays. Also can improvewith private variable to denote size of graph, and constructorargument to set size.Graph ADT: ImplementationAdjacency matrix method.Use N x N array of boolean values: 0 1 2 30 F T T F1 T F T F2 T T F T3 F F T F
  35. 35. (or can just use integer, and 1/0)If array name is G, then G[n][m] = T iff edge exists between node nand node m.Refrences structure notes on UMS STRUCTURES(Seymour lipschutz)