Heap Data Structure is generally taught with Heapsort. Heapsort algorithm has limited uses because Quicksort is better in practice. Nevertheless, the Heap data structure itself is enormously used. Following are some uses other than Heapsort.
Priority Queues: Priority queues can be efficiently implemented using Binary Heap because it supports insert(), delete() and extractmax(), decreaseKey() operations in O(logn) time. Binomoial Heap and Fibonacci Heap are variations of Binary Heap. These variations perform union also in O(logn) time which is a O(n) operation in Binary Heap. Heap Implemented priority queues are used in Graph algorithms like Prim’s Algorithm and Dijkstra’s algorithm.
Order statistics: The Heap data structure can be used to efficiently find the kth smallest (or largest) element in an array. See method 4 and 6 of this post for details.
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- Leaf starting point in a Binary Heap data structure
- Convert min Heap to max Heap
- Heap Sort for decreasing order using min heap
- Overview of Data Structures | Set 2 (Binary Tree, BST, Heap and Hash)
- Design an efficient data structure for given operations
- Applications of Priority Queue
- k largest(or smallest) elements in an array | added Min Heap method
- Tournament Tree (Winner Tree) and Binary Heap
- Time Complexity of building a heap
- Binomial Heap
- Why is Binary Heap Preferred over BST for Priority Queue?
- Fibonacci Heap | Set 1 (Introduction)
- How to check if a given array represents a Binary Heap?
- Check if a given Binary Tree is Heap
- K-ary Heap
- Heap in C++ STL | make_heap(), push_heap(), pop_heap(), sort_heap(), is_heap, is_heap_until()
- Implementation of Binomial Heap
- Where is Heap Sort used practically?
- Binary Heap
- Array Representation Of Binary Heap