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Find Most Frequent Element in an Array

Difficulty: Easy, Asked-in: Facebook, Uber.

Key takeaway: An excellent problem to learn problem-solving using sorting and hash table.

Let's understand the problem

Given an array X[] of size n, write a program to find the most frequent element in the array, i.e., the element that occurs the maximum number of times.

  • Assumed that at least one element is repeated.
  • If there are multiple elements with the maximum frequency, return the smallest of them.

Example 1

Input: X[] = [2, 12, 1, 2, 8, 2, 2, 1, 8, 2], Output: 2

Explanation: 2 is the most frequent element, which appears 4 times.

Example 2

Input: X[] = [1, 9, 1, 1, 2], Output: 1

Explanation: 1 is a single repeated element that appears 3 times.

Example 3

Input: X[] = [3, 8, 2, 3, 2], Output: 2

Explanation: 2 and 3 are repeated two times each. So we return the smallest of them, which is 2.

Discussed solution approaches

  • Brute force approach using nested loops
  • Using sorting and linear scan
  • Using hash table to store the frequency count

Brute force approach using nested loops

Solution idea

The basic idea is to count the occurrences of each element and return the element with the maximum number of occurrences. We can implement this using a nested loop where we pick each element using the outer loop and scan the entire array to count its frequency using the inner loop.

Solution steps

Step 1: We initialize two variables outside the nested loops: maxFreq to track the maximum frequency and mostFrequent to track the most frequent element.

Step 2: We run the outer loop from i = 0 to n - 1 to pick each element and the inner loop from j = 0 to n - 1 to count the frequency of that element. Before starting the inner loop, we also initialize a variable countFreq to track the frequency count of the current element.

  • Inside the inner loop, when X[i] = X[j], we increment countFreq by 1.
  • By the end of the inner loop, if maxFreq < countFreq, we found an element X[i] with a frequency greater than the maximum frequency count till that point. So we update maxFreq with countFreq and mostFrequent with X[i]. Otherwise, If countFreq == maxFreq, we update mostFrequent with the minimum of mostFrequent and X[i], i.e. min(mostFrequent, X[i]).

Step 3: By the end of the nested loop, we return the value stored in mostFrequent.

Solution pseudocode

int mostFrequentElement(int X[], int n)
{
    int maxFreq = 0
    int mostFrequent = -1
    
    for (int i = 0; i < n; i = i + 1)
    {
        int countFreq = 1
        
        for (int j = 0; j < n; j = j + 1)
        {
            if (X[j] == X[i])
                countFreq = countFreq + 1
        }
        
        if (maxFreq < countFreq)
        {
            maxFreq = countFreq
            mostFrequent = X[i]
        }
        else if (maxFreq == countFreq)
            mostFrequent = min(mostFrequent, X[i])
    }
    
    return mostFrequent
}

Time and space complexity analysis

We are running a nested loop and performing an O(1) operation at each iteration. Time complexity = n*n*O(1) = O(n^2). We are using a constant number of extra variables, so space complexity = O(1).

Using sorting and a single scan

Solution Idea

Now, the critical question is: How can we improve the time complexity? Searching is an essential operation in the problem. Can we think to improve the time complexity of searching to improve the overall complexity?

If we sort the array, then all duplicate elements will get placed adjacent to each other. We can easily scan the array, count the frequency of each element, and return the element with the max frequency. Compared to the above approach, this ensures that the frequency is calculated only once for each unique element.

Solution steps

Step 1: We first sort the array using some efficient O(nlogn) sorting algorithm like merge sort, heap sort, or quicksort. Let's suppose we are using heap sort, which works in place.

Step 2: We initialize two variables: maxFreq to track the maximum frequency and mostFrequent to track the most frequent element.

Step 3: Now we scan the sorted array using a loop until i < n. Inside the loop, we initialize the variable countFreq to track the frequency count of the current element.

  • We start from the first element and search for its consecutive occurrences using an inner while loop until X[i] != X[i + 1]. During this process, we increment countFreq and loop variable i by 1.
  • After the inner while loop, if maxFreq < countFreq, we have found an element X[i] with a frequency greater than the maximum frequency count up to that point. So, we update maxFreq with countFreq and mostFrequent with X[i]. Otherwise, if countFreq == maxFreq, we update mostFrequent with the min (mostFrequent , X[i]).
  • Now we go to the next iteration of the outer loop and repeat the same process for the next unique element available at index i.

Step 4: By the end of the outer loop, we return the value stored in mostFrequent.

Solution pseudocode

int mostFrequentElement(int X[], int n)
{
    sort(X, X + n)
    int maxFreq = 0
    int mostFrequent = -1
    
    int i = 0
    while (i < n)
    {
        int countFreq = 1
        
        while (i + 1 < n && X[i] == X[i + 1])
        {
            countFreq = countFreq + 1
            i = i + 1
        }
        
        if (maxFreq < countFreq)
        {
            maxFreq = countFreq
            mostFrequent = X[i]
        }
        else if (maxFreq == countFreq)
            mostFrequent = min(mostFrequent, X[i])
        
        i = i + 1
    }
    
    return mostFrequent
}

Time and space complexity analysis

If we observe the nested while loops, we are incrementing the value of i in each iteration of either the outer loop or the inner loop. So total number of nested loop iterations = n. In other words, we are accessing each element only once and performing O(1) operation. So time complexity of nested loop = n * O(1) = O(n).

The overall time complexity = Time complexity of heap sort + Time complexity of the nested loop = O(nlogn) + O(n) = O(nlogn). Space complexity = O(1), because heap sort works in place, and we are using a constant number of variables.

Using a hash table to store the frequency count

Solution idea and steps

Again, the critical question arises: How can we improve the time complexity? Can we use a hash table to efficiently perform searching? A hash table allows for basic dictionary operations such as insertion, deletion, and searching in O(1) average time. So, here is the idea:

  1. We create a hash table to store elements and their frequency counts as key-value pairs.
  2. Now, we scan the array to access each element X[i] and check if the value associated with X[i] exists in the hash table. If it exists, we increment the frequency count of X[i] by 1. If it does not exist, we store the frequency count of X[i] as 1.
  3. During the iteration, we also keep track of the most frequent element and current maximum frequency count using the variables mostFrequent and maxFreq. We update the values of these variables if we find frequency count of any element greater than maxFreq.
  4. Finally, we return the value stored in the variable mostFrequent.

Solution pseudocode

int mostFrequentElement(int X[], int n)
{
    HashMap<int, int> H
    int maxFreq = 1
    int mostFrequent = -1
    
    for (int i = 0; i < n; i = i + 1)
    {
        if (H.search(X[i]) == true)
        {
            H[X[i]] = H[X[i]] + 1
            
            if (maxFreq < H[X[i]])
            {
                maxFreq = H[X[i]]
                mostFrequent = X[i]
            }
            else if (maxFreq == H[X[i]])
                mostFrequent = min(mostFrequent, X[i])
        }
        else
            H[X[i]] = 1
    }
    
    return mostFrequent
}

Time and space complexity analysis

We perform a single scan of the array and, at each iteration, carry out constant operations. In other words, we search, insert, or update each element once in the hash table. So time complexity = n * O(1) = O(n). The space complexity = O(n) as we store the elements in the hash table.

Critical ideas to think!

  • Can we try to solve this problem using a BST? If yes, then what would be the time complexity?
  • Can we solve this problem using a different approach?
  • How can we further optimize the brute force approach? How do we ensure that the frequency is calculated only once for each unique element?
  • How can we optimize the second approach further? Is it possible to use binary search after sorting to find the most frequent element?
  • What if all elements were unique? What changes would you make?

Comparisons of time and space complexities

  • Using nested loops: Time = O(n^2), Space = O(1).
  • Using sorting and linear scan: Time = O(nlogn), Space = O(1).
  • Using hash table: Time = O(n), Space = O(n).

Suggested coding problems to practice

  • Find the most frequent word in a sentence
  • Sort characters by frequency
  • Find the frequency of all words in a sentence
  • Find the least frequent element in the array
  • Find the top k frequent elements in the array
  • Find the kth most frequent element in an array
  • Find the first non-repeating element in an array
  • Find the smallest missing positive integer in an array
  • Find the element that appears once in an array where every other element appears twice

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