Top K Frequent Elements Using Heap in DSA Javascript - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main idea behind using a heap to find the top K frequent elements?

Use a heap to keep track of the K elements with the highest frequency by pushing elements and popping the least frequent when size exceeds K.

beginner

What type of heap is commonly used to solve the Top K Frequent Elements problem efficiently?

A min-heap is used to keep the smallest frequency element at the top, so when the heap size exceeds K, the least frequent element can be removed.

beginner

Why do we use a frequency map before using a heap in this problem?

The frequency map counts how many times each element appears, which helps us know the frequency to compare when adding elements to the heap.

intermediate

In JavaScript, which data structure can be used to implement a min-heap for this problem?

JavaScript does not have a built-in heap, so we can implement a min-heap using an array and helper functions to maintain heap properties.

intermediate

What is the time complexity of finding the top K frequent elements using a heap?

The time complexity is O(N log K), where N is the number of unique elements, because each element is pushed and popped from the heap of size K.

What data structure is first used to count element frequencies before using a heap?

A Hash map (object or Map)

B Array

C Stack

D Queue

A hash map is used to count how many times each element appears.

Which heap type helps efficiently keep track of the top K frequent elements?

A Max-heap

B Fibonacci heap

C Min-heap

D Binary search tree

A min-heap keeps the smallest frequency at the top, so we can remove less frequent elements when size exceeds K.

If the heap size exceeds K, what operation is performed?

A Add more elements

B Remove the least frequent element

C Remove the most frequent element

D Clear the heap

We remove the least frequent element to keep only the top K frequent elements.

What is the main benefit of using a heap over sorting all elements by frequency?

A Heap is faster when K is much smaller than N

B Heap uses less memory

C Heap is easier to implement

D Heap sorts elements alphabetically

Heap is faster because it only keeps track of K elements instead of sorting all N elements.

What is the output of the algorithm?

A All elements sorted by frequency

B Top K frequent elements sorted descending

C Top K frequent elements sorted ascending

D Top K frequent elements in any order

The algorithm returns the top K frequent elements but order is not guaranteed.

Explain step-by-step how to find the top K frequent elements using a heap.

Think about counting first, then using a small heap to keep only K elements.

Why is a min-heap preferred over a max-heap for this problem?

Consider which element you want to remove when the heap grows too big.