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Brian Kim
Brian Kim

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DSA Pattern Recognition

#interview #computerscience #tutorial #algorithms

🧠 Complete DSA Pattern Recognition Lesson

How to Identify the Right Approach (End-to-End Guide)

🎯 Lesson Objective

By the end of this lesson, you will be able to:

  • Read a DSA problem and quickly identify its underlying pattern
  • Map keywords → data structures → algorithms
  • Eliminate brute force by instinctively choosing the right approach
  • Explain why a solution works (not just code it)

This is the difference between knowing syntax and thinking like an engineer.

🔑 The Core Truth About DSA

Almost every DSA problem is a remix.

The hard part is not coding — it’s recognizing the pattern fast enough.

There are ~12–15 core patterns that appear over and over.
Once you can recognize them, most problems become straightforward.

🧭 How to Use This Lesson

For every problem, ask yourself:

  1. What keywords stand out?
  2. Is the data contiguous, sorted, or relational?
  3. Am I optimizing, counting, searching, or generating?
  4. Is the problem asking for one answer or all answers?

1️⃣ Arrays & Strings

🔑 Keywords

  • subarray
  • substring
  • contiguous
  • range
  • window
  • longest / shortest

🧠 What This Signals

  • Sliding Window
  • Two Pointers
  • Prefix Sum

🛠 Typical Tools

  • Index pointers
  • Frequency hash
  • Running sum

✅ Example

“Find the longest substring without repeating characters”

→ Sliding Window + Hash Map

2️⃣ Sliding Window (Very High Frequency)

🔑 Keywords

  • longest / shortest
  • at most k
  • at least k
  • contains all
  • without repeating

🧠 Recognition Rule

If the problem asks for:

A contiguous segment + a condition

→ Sliding Window

🛠 Core Logic

  1. Expand window (right)
  2. Update state (counts, sum, etc.)
  3. If invalid → shrink window (left)
  4. Track best result

3️⃣ Two Pointers

🔑 Keywords

  • sorted array
  • pair
  • triplet
  • palindrome
  • remove duplicates

🧠 Recognition Rule

If:

  • The array is sorted
  • You compare elements from both ends

→ Two Pointers

🛠 Pointer Patterns

  • Opposite direction (left, right)
  • Same direction (slow / fast)

4️⃣ Hash Map / Hash Set

🔑 Keywords

  • frequency
  • count
  • duplicate
  • unique
  • seen before

🧠 Recognition Rule

If you need:

Fast lookup or counting

→ Hash Map / Hash Set

🛠 Often Combined With

  • Sliding Window
  • Prefix Sum
  • Graph traversal

5️⃣ Prefix Sum

🔑 Keywords

  • subarray sum
  • range sum
  • cumulative
  • equals k

🧠 Recognition Rule

If the problem involves:

Multiple overlapping sum queries

→ Prefix Sum

🛠 Core Idea

Store running totals so range queries are O(1).

6️⃣ Stack

🔑 Keywords

  • valid parentheses
  • balanced
  • next greater element
  • previous smaller

🧠 Recognition Rule

If elements need:

Matching, undoing, or nearest comparison

→ Stack

🛠 Variants

  • Monotonic Stack
  • Expression evaluation

7️⃣ Queue / Deque

🔑 Keywords

  • level order
  • sliding window maximum
  • stream
  • first unique

🧠 Recognition Rule

If items are processed:

In the order they arrive

→ Queue / Deque

8️⃣ Heap / Priority Queue

🔑 Keywords

  • top k
  • k largest / k smallest
  • most frequent
  • closest
  • merge k

🧠 Recognition Rule

If you need:

The best / worst element repeatedly

→ Heap

🛠 Common Uses

  • Scheduling
  • Streaming data
  • Greedy optimizations

9️⃣ Binary Search

🔑 Keywords

  • sorted
  • search
  • first / last occurrence
  • minimum possible
  • maximum possible

🧠 Recognition Rule

If:

  • Input is sorted or
  • The answer exists in a numeric range

→ Binary Search

🛠 Advanced Variant

Binary Search on Answer

Used when you search for the best feasible value.

🔟 Trees

🔑 Keywords

  • binary tree
  • depth
  • height
  • path
  • ancestor
  • BST

🧠 Recognition Rule

Tree problems almost always mean:

  • DFS (recursion)
  • BFS (level order)

1️⃣1️⃣ Graphs

🔑 Keywords

  • connected
  • path exists
  • cycle
  • islands
  • dependencies

🧠 Recognition Rule

If the problem describes:

Relationships or networks

→ Graph

🛠 Techniques

  • BFS / DFS
  • Topological Sort
  • Union Find

1️⃣2️⃣ Union Find (Disjoint Set)

🔑 Keywords

  • connected components
  • merge
  • dynamic connectivity
  • cycle detection

🧠 Recognition Rule

If you’re merging groups dynamically:
→ Union Find

1️⃣3️⃣ Dynamic Programming (DP)

🔑 Keywords

  • maximum / minimum
  • count ways
  • optimal
  • choices
  • overlapping subproblems

🧠 Recognition Rule

If the problem:

  • Has repeated subproblems
  • Requires an optimal result

→ DP

1️⃣4️⃣ Greedy

🔑 Keywords

  • minimum operations
  • maximize profit
  • earliest / latest
  • best local choice

🧠 Recognition Rule

If a local optimal choice leads to a global solution:
→ Greedy

1️⃣5️⃣ Backtracking

🔑 Keywords

  • all combinations
  • all permutations
  • generate
  • explore

🧠 Recognition Rule

If the problem asks for:

All possible solutions

→ Backtracking

1️⃣6️⃣ Bit Manipulation (Bonus)

🔑 Keywords

  • xor
  • single number
  • power of two
  • bitmask

🧠 Recognition Rule

If the problem hints at binary logic:
→ Bit Manipulation

🚀 Final Interview Checklist

Ask yourself:

  1. Is it contiguous? → Sliding Window
  2. Is it sorted? → Binary Search / Two Pointers
  3. Do I need fast lookup? → Hash Map
  4. Am I choosing the best k items? → Heap
  5. Is it all possibilities? → Backtracking / DP
  6. Are there relationships? → Graph / Union Find

🧠 Final Thought

You don’t master DSA by memorizing solutions.

You master it by recognizing patterns instantly.

Once pattern recognition clicks,

the code becomes the easy part.

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