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ToggleIntroduction to Linear and Non Linear Data Structure
In Linear and Non Linear data structure, data components are set up in a linear sequence where every single element is connected to the element before or after it. Data components are connected in a proper hierarchical order or even in different patterns in data structures like linear and non linear data structure.
At Technical Faculty Munich, Klaus Samelson and Friedrich L. Bauer proposed the idea back in 1955 and they filed a patent in 1957. The same idea was implemented independently by an Australian named Charles Leonard Hamblin in the beginning of 1957.
What is Data Structure?
Data structures are methods of storing data in your runtime memory for optimal data utilization. Data Structures have a wide scope of usage in the field of computer science and in order to develop your skills as a programmer it is important that you possess a good knowledge of data structures.
Data Structures are basically a specialized way of organizing and storing data in computers in a way that you can perform operations on the stored data in a more efficient manner. In simple language, Data Structures are structures programmed to store ordered data. It represents the knowledge of Data to be organized in memory . It should be designed and implemented in such a manner that it reduces the complexity and increases the efficiency.
The term data means a value or set of values. It specifies either the value of a variable or a constant (e.g., marks of students, name of an employee, address of a customer, value of pi, etc.)
While a data item that does not consist of a subordinate data item is categorized as an elementary item, the one that comprises one or more subordinate data items is called a group item. For example, a student’s name may be divided into three sub-items—first name, middle name, and last name—but his roll number would normally be treated as a single item.
Data structures are generally categorized into two classes: primitive and non-primitive data structures.
Primitive & Non-Primitive Data Structures
Primitive data structures are the basic data types which are supported by a programming language. Some basic data types are integer, real, character, and boolean. The terms ‘data type’, ‘basic data type’, and ‘primitive data type’ are often used interchangeably.
Non-primitive data structures are those data structures which are created using primitive data structures. Examples of such data structures include linked lists, stacks, trees, and graphs. Non-primitive data structures can further be divided into two categories: linear and non-linear data structures.
Types of Data Structure
Data Structures are broadly classified into two different categories:
1) Linear data structure
2) Non-linear data structure
What is Linear Data Structure?
- Linear Data Structures – In these data structures, the different elements are arranged in a sequence one after the other. These are easy to implement as there is no particular order to them. But it is important to note that due to the operational complexities, using linear data structures is not recommended when there is growth in the complexity of the program.
Linear data structures are those that store data in a linear fashion, i.e. one after the other or simply in one dimension. They are subdivided into two categories - Static Data Structures – Are those whose size cannot be modified after they have been defined. An array is an example.
- Dynamic data structures – Are those whose size can change even after they are declared. Linked List is one example.
Linear Data Structure Types
1. Array
A linear data structure that collects components of the similar data type and stores them in adjacent memory places is known as an array. Arrays use an index system that ranges from 0 to (n-1), in which n is the size of the array.
2. Linked List
Linked list is a form of linear data structure which consists of a sequence of linked nodes. Each node holds the data as well as the address of the next node.
3. Queue
Queues, like Stacks, are abstract data structures. Unlike a stack, a queue has two endpoints that are open. One end is always used to input data, whereas the other end is always used to remove data . The queue uses the Initially-In-First-Out (FIFO) method, which means that the data item that was stored first gets accessible first.
4. Stack
A stack is a type of linear data structure in which operations are done in a specific order. The order might be LIFO (last-in, first-out) or FILO (First-In, Last-Out).
What is a Non-Linear Data Structure?
Non-linear Data Structures – The characteristics of a non-linear data structure are not present in any particular sequence. Rather, these elements are arranged in a manner where one element is connected to one or more other elements.
These non-linear data structures are differentiated into Graph and Tree data structures. Non-linear data structures are data structures that hold data in several dimensions.
Non Linear Data Structure Types
Non- Linear Data Structure are divided into two types
1. Graphs
A graph is a nonlinear data structure made up of nodes and edges. The nodes are also known as vertices, while edges are lines or arcs that connect two nodes in the graph.
Some popular Graph based data structures are –
- Spanning Tree and Minimum Spanning Tree
- Adjacency Matrix
- Strongly Connected Components
2. Trees
A tree is a hierarchical data structure consisting of nodes. Nodes represent value, and nodes are linked together by edges.
Some of the Tree based data structures are –
- Binary Tree
- B+ Tree
- B- Tree.
Difference between Linear and Non Linear Data Structure
Some major differences between linear and non linear Data Structure have been mentioned in a table and are as follows.
Linear Data Structure | Non Linear Data Structure |
The linear relationship is present between data elements. | Data elements have a hierarchical relationship. |
Every element makes a connection to only its previous and next elements. | It is more broader and every element makes a connection to more than two elements. |
It is a single-level data structure. | It is a multi-level data structure. |
The utilization of memory is not that efficient. | Memory utilization is very much efficient. |
We can traverse it in a single run as it is linear. | We can’t traverse it in a single run as it is a non-linear structure. |
It is easy to implement and use. | It is complex to implement. However, we can implement it easily, as nonlinear data structures include a powerful algorithm to implement them. |
Time complexity increases if we need to traverse through the large dataset. | There is not much change in time complexity if we need to traverse through the large input size. |
It is used to build an operating system and compiler design. | non-linear data structure covers all aspects of social networks, telephone networks, Artificial intelligence, and image processing |
Linear Data structure Examples: Array, stack, queue, Linked List | Non linear Data Structure Examples: Graph, map, tree, heap data structure |
Also Read: Clustering Algorithms in Data Mining
Advantages of Linear and Non Linear Data Structure
There are several advantages of linear and non linear data structure. Let us deep dive into linear and non linear data structure one-by-one.
Linear Data Structure
Stack is a part of Linear Data structure and it is one of the most important Linear Data Structures. You will have a clear understanding about the advantages of Linear Data structure if you have a good understanding about Stack.
Talking about the advantages of stack, Effective data management: A linked list and an array cannot be used to handle data in a LIFO (last in, first out) manner. Stack makes this possible. Effective function management: Local variables are placed on a stack when a function is called, and the stack is destructed once the function has completed.
Memory management: Stack enables you to manage memory allocation and deallocation. Stack automatically cleans up the object, demonstrating clever memory management. It is not readily corrupted: Stack is more safe and trustworthy since it is not easily corrupted. It doesn’t permit variable resizing: Variables cannot be resized.
Stack is a linear data structure that works on the principle of LIFO(Last In First Out), which means the element that is inserted at last would be removed at first from it. Consider an example of a pile of plates kept in a wedding buffet. The plate that was added at the last is removed first and that is from the top of the pile. Hence, in a stack, operations are performed on one end only.
There are three primary operations they do:
- Push() moves an additional element to the top of the stack.
- Pop() removes and returns the stack’s top-most element.
- Top() and peek() search across the stack and return the most recent element added to it.
The stack is not changed by this operation. Stack data structures come very handy when the sequence of events matters. They make sure that a system doesn’t do the previous action before moving on to the next. Here are some typical instances of stack usage:
- Reversing — A data stack will by default reverse any input. If we wanted to invert the string “Hello World,” for instance. Each character would be push()ed onto the stack, and subsequently it would be pop()ed off.
- Undo/Redo — This method can be used in editors to add the functionality of undoing and redoing actions. Every time a change is made, the program’s state can be put to a stack. Use pop() to undo the most recent modification.
- Back-Tracking -When creating an algorithm to solve a problem involving choosing paths, such as a maze, backtracking might be employed. If a path leads to a dead end, the last branch in the path must be eliminated (popped()), and a new path must be determined. A path is added to the stack each time it is selected.
- Call Stack –Programming languages employ a call stack, or data stack, to run code. A function is called, added to the call-stack, and then removed after it is finished.
Non Linear Data Structure
Binary search is a part or Non linear Data structure search has a better asymptotic time complexity than linear search, meaning that it scales better with input size. However, it requires the data to already be sorted and requires random-access capability . for example, an array can do this but a linked list cannot). Linear search does not have these requirements, and is also simpler.
Generally, if your data is a sorted array you would use binary search; otherwise, use linear search. Unless, of course, your data is in some other type of searchable collection such as a tree, in which case you would use that collection’s search procedure. Since linear search is slow, if you are doing repeated linear searches against large collections, you should consider sorting the data or placing the data in some efficiently searchable data structure.
Disadvantages of Linear and Non Linear Data Structure
Linear Data Structure
Now coming to the disadvantages of Linear Data structure in terms of stack, Small memory size: Stack memory is quite constrained. Stack overflow probabilities: Placing an excessive number of objects on the stack can make it more likely. Random access is not possible: It is not possible to access data randomly in a stack.
Unreliable: When variable storage is rewritten, it can occasionally cause a function or programme to behave in an undefinable way. Unwanted termination: If the stack exits the memory region, it could result in an odd termination.
Non-Linear Data Structure
Coming to the disadvantages of Non Linear Data Structure in terms of Binary search. It is more confusing than a simple inquiry and is excessively excessive for small amounts of components. It only functions with organized and maintained records. It only functions with component kinds for which there are fewer relationships. Some types can’t really be organized.
If the list does not allow for unauthorized access, there is a significant loss of effectiveness. For instance, you want to quickly move to the middle of the rundown. That is ideal if your rundown is a simple exhibit. In the unlikely event that there is a related rundown, no. The cost of negotiating a non-irregular access rundown may outweigh the cost of the exams, depending on the cost of your correlation activity.
Hash searches are one of the much faster search methods available. In any case, a hash query necessitates the coordination of the parts in a significantly more complicated information structure.
Conclusion
Data is the next big thing in the coming time . As applications are becoming more confusing and the amount of data rises day by day, which may cause errors with searching data, processing speed, handling multiple requests etc. Data Structuring is very essential, hence linear and non linear data structure comes into picture.
Data structure provides a way of managing, organizing and storing data in an efficient manner. With the help of data structures the data items can be accessed easily and there are two types of data structure namely linear and non linear data structure.
Data structure provides reusability, efficiency and abstraction in different ways like linear and non linear data structure. It plays a crucial role in improving the performance of programs because the main function of the programs are to store and retrieve the user’s data as quickly as possible.
Frequently Asked Question's
There are few differences with regards to linear and non linear data structure. In linear data structure, the elements (data items) are being stored and retrieved in a sequential or a linear manner. The location of those elements need not to be contiguous in linear and non linear data structure. In 1D Array the elements are contiguous but in 2D Array when allocated on heap (by new keyword) they are not contiguous. Similar is the case of Linked Lists. And the ADT such as Stacks and Queue are also linear but not contiguous.
In linear and non linear data structure the non linear data structures on the other hand are which data is not organized in a sequential fashion. The best example of this kind of data structure is Graph, it can be used to define the relation between the two data and a single data can have multiple relationships with other data.
Tree is also a graph and it is best described by parent child relationship. Hence, both linear and non linear data structure have their own attributes.
Between linear and non linear data structure. If we talk about linear data structure. A linear data structure has data elements that are arranged in a sequential manner. Each element is connected to its last and next element.
There are various operations that are performed on a linear data structure. If you compare the following steps in linear and non linear data structure, it best suits the former one. Some of the operations are given below:
- Traversing : Processing each element in linear data structure at least once.
- Merging : Combining the two in one is called merging.
- Search : Finding the location of the given element
- Deletion : Removing an element from the list
- Sorting : Arranging the element in some order
- Insertion : Adding a new element in the data structure.
In linear and non linear data structure this is about Linear data structure.
When we talk about linear and non linear data structure. They consist of various types which together form a linear and non linear data structure. Following are the 4 types of linear data structures:
- Array- A linear data structure that collects components of the similar data type and stores them in adjacent memory places is known as an array. Arrays use an index system that ranges from 0 to (n-1), where n is the size of the array.
- Linked List- A linked list is a set of linear data structures that consists of a sequence of linked nodes. Each node holds the data as well as the address of the next node.
- Queue- Queues, like Stacks, are abstract data structures. Unlike a stack, a queue has two endpoints that are open. One end is always used to input data , whereas the other end is always used to remove data .
The queue uses the Initially-In-First-Out (FIFO) method, which means that the data item that was stored first gets accessible first. - Stack- A stack is a type of linear data structure in which operations are done in a specific order. The order might be LIFO (last-in, first-out) or FILO (First-In, Last-Out).
In linear and non linear data structure these are the 4 types of Linear Data Structure.
Under linear and non linear data structure we have different types . These types differentiate between linear and non linear data structure. Following are some of the examples of non linear data structures
a) Graphs- A graph is a nonlinear data structure which comprises nodes and edges. The nodes are also known as vertices, while edges are arcs or lines that connect any two nodes in the graph. Some popular Graph based data structures are –
- Spanning Tree and Minimum Spanning Tree
- Adjacency Matrix
- Strongly Connected Components
b) Trees- A tree is a hierarchical data structure consisting of nodes. Nodes represent value, and nodes are linked together by edges. Some of the Tree based data structures are –
- Binary Tree
- B+ Tree
- B- Tree
In linear and non linear data structure the graphs and trees are two most common type of non linear data structure.
There are two main types of data structures. They are linear and non linear data structure. Both linear and non linear data structure have their own features and are further sub- divided into further parts.
Linear Data Structure
In these data structures, the different elements are arranged in a sequence one after the other. These are easy to implement as there is no particular order to them.
But it is important to note that due to the operational complexities, using linear data structures is not recommended when there is a surge in the complexity of the program.Linear data structures are those that store data in a linear fashion, i.e. one after the other or simply in one dimension.
Non Linear Data Structure
The elements of a non-linear data structure are not present in any particular sequence. Rather, these elements are arranged in a hierarchical manner where one element is connected to one or more other elements.
These non-linear data structures are differentiated into Graph and Tree data structures. Non-linear data structures are data structures that hold data in several dimensions.
To know more about linear and non linear data structure you can read the complete blog.
Types of linear and non linear data structure forms a set of data structures. Both linear and nonlinear data structure slightly vary from each other
A data structure where data items are not organized serially is called non linear data structure. In other words, data elements of the non linear data structure could be connected to more than one element to reflect a special relationship among them. All the data elements in non linear data structure can not be traversed in a single run.
So, in arrays, stack, queue, and linked list, the data items are organized linearly, like they have a sequence of data items, such as after data A, comes data B, data C and so on.
But trees and graphs are called non linear data structures because there is no such sequence, like after data A, comes data B or C based on the situation and condition.
Hence this is some information about linear and non linear data structure.
Some of the major differences between linear and non linear data structure are as follows.
1.) The linear relationship is present between data elements but in non linear data elements have a hierarchical relationship.
2.) In a linear data structure .Every element makes a connection to only its previous and next elements.
3.) A non linear data structure is more broader and every element makes a connection to more than two elements.
4.) A linear data structure is a single-level data structure, whereas a non linear data structure is a multi-level data structure.
5.) A linear data structure is easy to implement and us, but a non linear data structure is complex to implement. However, we can implement it easily, as nonlinear data structures include a powerful algorithm to implement them.
These are some of the differences between linear and non linear data structure.
Both the linear and non linear data structure have their own uses.
An array is a linear data structure. It can also be said that an array forms a crucial part in linear and non linear data structure . In computer programming, the majority of scenarios demand storing large amounts of data of the same type.
Arrays are ideal for storing numerous values in a single variable. We must define a lot of variables in order to hold this much data. To keep track of all the variable names while coding programmes would be exceedingly challenging. It is preferable to define an array and put all the elements inside of it rather than giving each variable a different name.
In other words, between linear and non linear data structure, an array plays an important role in linear data structure.
There are various linear and non linear data structure but Tree is an example of non linear data structure. A tree is a data structure similar to a linked list but instead of each node pointing simply to the next node in a linear fashion,each node points to a number of nodes
- The tree structure is useful because it easily accommodates the creation and deletion of folders and files(An operating system maintains a disk’s file system as a tree).
- Make information easy to search(tree traversal)
- Router algorithms
- We can insert/delete keys in moderate time (quicker than Arrays and slower than Unordered Linked Lists).
- Trees are used to represent phrase structure of sentences, which is crucial to language processing programs
Thus, it is one of the most important subsets under linear and non linear data structure
One of the most important features which comes under a linear data structure which forms a part of a linear and non linear data structure
A queue is a linear data structure. A queue is open at both the ends and functions according to the FIFO {First In First Out} principle. At one end of the queue, known as the back end, data insertion is performed, while at the other end, known as the front end or head of the queue, data deletion is performed. If we must conceive of a line or queue in general, we can use the example of the one outside a ticket counter.
What goes on in the line? The first person to receive a ticket is that person who is standing in front of the line. The individual then exits the line. The newcomer always starts at the back of the line. Thus, this makes a queue one of the highlights under linear and non linear data structure.