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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- Why is Binary Heap Preferred over BST for Priority Queue?
- k largest(or smallest) elements in an array | added Min Heap method
- Binary Heap
- Time Complexity of building a heap
- Binomial Heap
- Sort a nearly sorted array using STL
- Python Code for time Complexity plot of Heap Sort
- Priority Queue in Python
- Priority queue of pairs in C++ (Ordered by first)
- Kth smallest element after every insertion