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Hash Table Applications

Hash tables are versatile data structures with numerous applications in algorithmic problem-solving.

Frequency counting​

  • Character Frequency: Count occurrences of each character in string
  • Element Frequency: Count occurrences of each element in array
  • Word Frequency: Count word occurrences in text
  • Pattern Frequency: Count pattern occurrences in data

Lookup optimization​

  • Fast Lookup: O(1) average case for finding elements
  • Two Sum: Finding pairs that sum to target
  • Complement Lookup: Finding complement of current element
  • Index Mapping: Mapping values to their indices

Caching & memoization​

  • Dynamic Programming: Storing computed results to avoid recalculation
  • Function Memoization: Caching function results
  • LRU Cache: Least recently used cache implementation
  • Fibonacci Memoization: Optimizing recursive calculations

Grouping & categorization​

  • Group Anagrams: Grouping strings by their character frequencies
  • Group by Key: Grouping elements by specific property
  • Partitioning: Dividing data into categories
  • Clustering: Grouping similar elements together

Duplicate detection​

  • Find Duplicates: Identifying duplicate elements in array
  • Remove Duplicates: Eliminating duplicate entries
  • First Duplicate: Finding first occurrence of duplicate
  • Duplicate Count: Counting number of duplicates