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$ curl repovive.com/roadmaps/data-structures/heaps-priority-queues/top-k-frequent-implementation

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$ curl repovive.com/roadmaps/data-structures/heaps-priority-queues/top-k-frequent-implementation

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Top K Frequent - Implementation - Heaps & Priority Queues | Data Structures | Repovive

Repovive.

Data Structures19 sections · 729 units49h total

Open in Course

Top K Frequent - Implementation

Complete solution

Here's the solution with a min-heap of size $k$ :

function topKFrequent(nums, k)
    // Phase 1: Count frequencies
    freq := empty hash map
    for each num in nums
        freq[num] := freq[num] + 1

    // Phase 2: Find top k by frequency
    heap := empty min-heap of (frequency, element) pairs

    for each (element, count) in freq
        if size of heap < k
            insert (count, element) into heap
        else if count > frequency at heap root
            extract min from heap
            insert (count, element) into heap

    return all elements from heap

Time: $O(n \log k)$ . Space: $O(n)$ .