Top K Frequent Elements Using Heap in DSA Go - 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

In Go, which heap type is commonly used to find top K frequent elements efficiently?

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

intermediate

Why do we use a min-heap instead of a max-heap for the top K frequent elements problem?

A min-heap keeps the smallest frequency at the top, allowing easy removal of the least frequent element when the heap size exceeds K, maintaining only the top K frequent elements.

beginner

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

A map (dictionary) is used to count how many times each element appears in the input list.

intermediate

Explain the time complexity of finding top K frequent elements using a heap.

Counting frequencies takes O(N). Building and maintaining a heap of size K takes O(N log K). Overall complexity is O(N log K), efficient for large N and small K.

What is the first step in finding the top K frequent elements using a heap?

A Remove duplicates

B Build a max-heap of all elements

C Sort the array

D Count the frequency of each element using a map

We first count how many times each element appears using a map before using a heap.

Which heap type is best to maintain the top K frequent elements efficiently?

A Max-heap

B Min-heap

C Binary search tree

D Stack

A min-heap keeps the smallest frequency on top, so we can remove the least frequent element when size exceeds K.

If the heap size exceeds K, what should you do?

A Remove the element with the lowest frequency

B Remove the element with the highest frequency

C Add more elements

D Clear the heap

Remove the element with the lowest frequency to keep only the top K frequent elements.

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

A O(N^2)

B O(N log N)

C O(N log K)

D O(K log N)

Counting frequencies is O(N), and maintaining a heap of size K is O(N log K), so overall O(N log K).

What data structure is used to store the frequency counts before pushing to the heap?

A Map (dictionary)

B Array

C Queue

D Linked list

A map stores each element's frequency count efficiently.

Describe the step-by-step process to find the top K frequent elements using a heap.

Think about counting first, then using a heap to keep only the most frequent.

Explain why a min-heap is preferred over a max-heap in the top K frequent elements problem.

Focus on how to keep the heap size limited to K.