Top K Frequent Words

Top K Frequent Words

What is this problem about?

The "Top K Frequent Words coding problem" is an extension of the frequency counting pattern specifically applied to text data. You are given a list of words and asked to return the k most frequent ones. However, there's a crucial tie-breaking rule: if two words have the same frequency, they must be sorted in alphabetical (lexicographical) order. This adds a layer of complexity to the sorting or heap management, as you are now dealing with two different comparison criteria simultaneously.

Why is this asked in interviews?

Tech giants like Amazon, Google, and Netflix favor this "Top K Frequent Words interview question" because it's a realistic simulation of features like "Trending Topics" or "Word Clouds." It tests your ability to implement custom comparators and manage complex data types. Interviewers look for clean handling of the lexicographical requirement and efficient use of memory. It's not just about counting; it's about presenting data in a specific, user-centric order.

Algorithmic pattern used

Like its integer counterpart, this problem relies on the "Hash Table" for initial counting. To handle the selection and tie-breaking, the "Heap (Priority Queue)" with a custom comparator is the most common "Trie, Heap (Priority Queue), String interview pattern." A Max-Heap can be used to store all unique words and their counts, or a Min-Heap of size k can be maintained for better efficiency. Using a "Trie" is an alternative for very large datasets where prefix matching or alphabetical sorting is a priority.

Example explanation

Imagine you have the words ["i", "love", "coding", "i", "love", "java"] and k = 2.

1. Frequency Map: {i: 2, love: 2, coding: 1, java: 1}.
2. Comparison: Both "i" and "love" appear twice. To decide the order, we look at them alphabetically. "i" comes before "love".
3. Selection: The top 2 most frequent words, sorted correctly, are ["i", "love"]. If the words were ["apple", "pear", "apple", "pear"], the result would be ["apple", "pear"] because 'a' comes before 'p'.

Common mistakes candidates make

The most common error is neglecting the tie-breaking alphabetical order. Many candidates simply return the top k by frequency in any order. Another mistake is using a simple sort on the entire list of unique words, which is $O(M \log M)$ where $M$ is the number of unique words. While often acceptable, it's less optimal than a heap-based approach for large datasets.

Interview preparation tip

Practice writing custom comparator functions in your preferred language. For the "Top K Frequent Words coding problem," your comparator should return based on the frequency difference first, and if that is zero, it should return based on the string comparison. Understanding how your language's priority queue handles these custom rules is vital for success.