Top K Frequent Elements | Leetcode #347 | Heap | Hashmap

This video explains a very important programming interview problem which is to find the top k frequent elements in a given array.This problem is based on heap and hashmap.I have shown all the required concepts by showing the intuition using examples.I have first explained the simple solution and then optimized to give a better solution.I have explained the problem in two different ways using heap and hash and i have also compared the efficiency with each other and showed which is better and why.

CODE LINK is present below as usual. If you find any difficulty or have any query then do COMMENT below. PLEASE help our channel by SUBSCRIBING and LIKE our video if you found it helpful...CYA :)

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[amanvijayvargiya]

Finally I am placed in delhivery company with decent package ...Thanks uuh so much .You inspired me a lot .I will always remember you.

[ankitbatra]

Finally, got to see the man behind all these great videos! Thank you so much for contributing! :)

[neerajpandey]

The explanation to choose between first (KlogN) and second(NlogK) was super.

[anikkhan]

Your Time Complexity analysis is top-notch!!! 😍😍 Plz continue explaining time complexity in your future videos too.

[darshpreetnarula]

No youtube video
has explained it better than you.Thanks!

[AlexZaharia]

You assume that adding elements to the heap is O(K) or O(N) which in fact will be O(KlogK) and O(NlogN) respectively. That means that the second heap algo is better than the first one.

[jayaprakashch]

Why we need to sort the hashmap? in 5.30min, instead will loop through each element in map and compare the val with k. if val is grater then equal to k then add that element to list and return, the time comlexity will be O(2n)=> O(n)

[ommapari]

I love you, sir, I became a fan of your explanations... I really want to meet you someday 😢😢

[nayanjain]

Sir, please correct me if I am wrong, for build heap (line 26-29) it's going to take O(nLog(n)) time as for all n elements you are pushing into a queue (pushing is a Log(n) operation). So O(nLog(K)) will be best approach to use here.

[hillarioushollywood]

The question arises that how to make heapify based on values and not on keys? Because, The heapify() on tuples considers the first element in the tuple for the process

[tulika]

If we use a max heap, will it not be 0(N + KlogN)? N for heapify and KLogN for popping K times.

[MalladiSankar]

I think we should use minheap isn't it? we need to pop from the root all the smaller values(frequencies with small values first) and add high frequency ones to the heap

[priytoshtripathi]

A better solution would be to use Bucket sort technique, after counting the frequencies.We'll be able to solve it O(n)

[MahirHasancse]

Why do you use a.freq<b.freq instead of using a.freq>b.freq?

[jaagrataggarwal]

will heap not take O(nlogn) for insertion? You said to insert n elements, heap would take O(n).But for rearranging after every insertion it takes logn time. So would not it take O(nlogn) time for inserting n elements?

[pottalokesh]

Also can you explain something about that comparator function in next video ...

[shruti]

What do you use for drawing on the canvas?

[princeanthony]

The interviewer at Google expected a bucket sort solution in my case. He didn’t accept using a heap.

[vasanthkumar]

Build heap is N log N times because of heapify up

[SiddharthKulkarniN]

There is also a O(N) solution using bucket sort, isn't it?