Top K Frequent Elements Using Heap in DSA Typescript - 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 the 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 TypeScript, which data structure can be used to implement a min-heap for this problem?

A priority queue or a custom binary heap class can be implemented to act as a min-heap since TypeScript does not have a built-in heap.

intermediate

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

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

What does the heap store in the Top K Frequent Elements problem?

A Elements with their frequencies

B Only elements without frequency

C Only frequencies without elements

D Sorted list of all elements

The heap stores pairs of elements and their frequencies to compare and keep track of the top K frequent elements.

Why do we remove elements from the heap when its size exceeds K?

A To keep only the K most frequent elements

B To sort the elements

C To count frequencies

D To convert heap to array

Removing elements when heap size exceeds K ensures only the K elements with highest frequency remain.

Which data structure is used to count the frequency of elements before using the heap?

A Array

B Hash map or dictionary

C Stack

D Queue

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

What is the role of a min-heap in this problem?

A To sort elements alphabetically

B To keep the most frequent element on top

C To keep the least frequent element on top for easy removal

D To store all elements

The min-heap keeps the smallest frequency element on top so it can be removed when heap size exceeds K.

What is the overall time complexity of this approach?

A O(N)

B O(N^2)

C O(K log N)

D O(N log K)

Building the frequency map is O(N), and pushing/popping from a heap of size K for N elements is O(N log K).

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

Think about counting first, then keeping only K elements with highest counts.

Describe why a min-heap is preferred over a max-heap for this problem.

Focus on how to efficiently keep only K elements.