Union Find Disjoint Set Data Structure in DSA C - Time & Space Complexity

Time Complexity: Union Find Disjoint Set Data Structure

O(α(n))

Understanding Time Complexity

We want to understand how fast the Union Find data structure works when connecting and checking groups.

How does the time needed change as we add more items?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

// Find with path compression
int find(int parent[], int x) {
    if (parent[x] != x) {
        parent[x] = find(parent, parent[x]);
    }
    return parent[x];
}

// Union by rank
void unionSets(int parent[], int rank[], int a, int b) {
    a = find(parent, a);
    b = find(parent, b);
    if (a != b) {
        if (rank[a] < rank[b])
            parent[a] = b;
        else if (rank[b] < rank[a])
            parent[b] = a;
        else {
            parent[b] = a;
            rank[a]++;
        }
    }
}
    

This code finds the group leader of an element and merges two groups efficiently.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: The recursive find function that follows parent links to find the root.
• How many times: Each find call may traverse up the tree, but path compression flattens it over time.

How Execution Grows With Input

Initially, finding a root may take longer if trees are tall, but path compression and union by rank keep trees shallow.

Input Size (n)	Approx. Operations per find/union
10	About 4-5 steps or less
100	About 5-6 steps or less
1000	About 6-7 steps or less

Pattern observation: The steps grow very slowly, almost constant, even as input grows large.

Final Time Complexity

Time Complexity: O(α(n))

This means each operation takes almost constant time, where α(n) is a very slow growing function.

Common Mistake

[X] Wrong: "Each find or union takes linear time because it may traverse many parents."

[OK] Correct: Path compression and union by rank keep trees very flat, so operations are almost constant time, not linear.

Interview Connect

Understanding this complexity shows you can handle efficient grouping problems, a common skill in many coding challenges.

Self-Check

"What if we removed path compression? How would the time complexity change?"

Practice

1. What is the main purpose of the Union Find Disjoint Set data structure?

easy

A. To sort elements in ascending order

B. To efficiently track and merge groups of connected elements

C. To store key-value pairs for fast lookup

D. To implement a queue with priority

Solution

1. Step 1: Understand the role of Union Find

   The Union Find data structure is designed to keep track of which elements belong to which groups or sets, especially when these groups merge.

2. Step 2: Compare with other data structures

   Sorting, key-value storage, and priority queues serve different purposes unrelated to grouping connected components.

3. Final Answer:

   To efficiently track and merge groups of connected elements -> Option B

4. Quick Check:

   Union Find = track and merge groups [OK]

Hint: Union Find = manage connected groups fast [OK]

Common Mistakes:

• Confusing Union Find with sorting algorithms
• Thinking it stores key-value pairs
• Assuming it manages priority queues

2. Which of the following is the correct way to initialize a Union Find parent array for n elements in C?

easy

A. int parent[n]; for(int i=0; i

B. int parent[n]; for(int i=0; i

C. int parent[n]; for(int i=0; i

D. int parent[n]; for(int i=0; i

Solution

1. Step 1: Understand initialization of Union Find

   Each element starts as its own parent, so parent[i] = i for all i from 0 to n-1.

2. Step 2: Check each option

   int parent[n]; for(int i=0; i

3. Final Answer:

   int parent[n]; for(int i=0; i -> Option A

4. Quick Check:

   Initialize parent[i] = i [OK]

Hint: Parent array starts with self indices [OK]

Common Mistakes:

• Setting all parents to zero
• Using -1 as parent without meaning
• Assigning parent to n (out of range)

3. Consider the following C code using Union Find for 5 elements (0 to 4):

int parent[5];
for(int i=0; i<5; i++) parent[i] = i;

// Union operations
union_set(parent, 0, 1);
union_set(parent, 1, 2);
union_set(parent, 3, 4);

// Find operations
int a = find_set(parent, 2);
int b = find_set(parent, 4);

printf("%d %d", a, b);

Assuming union_set merges sets and find_set returns the root parent, what is the printed output?

medium

A. 0 3

B. 1 3

C. 2 4

D. 0 4

Solution

1. Step 1: Trace union operations

   Initially, parent[i] = i for i=0..4.
   union_set(0,1) merges sets containing 0 and 1, root becomes 0.
   union_set(1,2) merges sets containing 1 and 2, root is 0 (since 1's root is 0).
   union_set(3,4) merges sets containing 3 and 4, root becomes 3.

2. Step 2: Trace find operations

   find_set(2) returns root of 2, which is 0.
   find_set(4) returns root of 4, which is 3.

3. Final Answer:

   0 3 -> Option A

4. Quick Check:

   Roots after unions: 2->0, 4->3 [OK]

Hint: Follow union roots to find final parents [OK]

Common Mistakes:

• Assuming find_set returns the element itself
• Not updating roots after unions
• Mixing up which element is root

4. The following C code attempts to implement union_set but has a bug:

void union_set(int parent[], int a, int b) {
    int rootA = find_set(parent, a);
    int rootB = find_set(parent, b);
    if (rootA != rootB) {
        parent[a] = rootB;
    }
}

What is the bug in this code?

medium

A. It should assign parent[rootB] = rootA instead

B. It does not check if rootA equals rootB before union

C. It assigns parent[a] instead of parent[rootA], breaking the set structure

D. It uses find_set incorrectly by passing wrong parameters

Solution

1. Step 1: Analyze union assignment

   The union should link the root of one set to the root of the other.
   Assigning parent[a] = rootB is wrong because 'a' may not be a root.

2. Step 2: Correct union assignment

   It should assign parent[rootA] = rootB to merge sets properly.
   This maintains the tree structure of the disjoint sets.

3. Final Answer:

   It assigns parent[a] instead of parent[rootA], breaking the set structure -> Option C

4. Quick Check:

   Union must link roots, not elements [OK]

Hint: Always link root parents in union [OK]

Common Mistakes:

• Assigning parent of non-root element
• Skipping root equality check
• Confusing parameters in find_set

5. You have 7 elements (0 to 6) initially separate. After these union operations:

union_set(parent, 0, 1);
union_set(parent, 1, 2);
union_set(parent, 3, 4);
union_set(parent, 5, 6);
union_set(parent, 2, 5);

How many distinct sets remain?

hard

A. 5

B. 4

C. 3

D. 2

Solution

1. Step 1: Track unions step-by-step

   Initially: 7 sets: {0}, {1}, {2}, {3}, {4}, {5}, {6}
   union(0,1): {0,1}, {2}, {3}, {4}, {5}, {6}
   union(1,2): {0,1,2}, {3}, {4}, {5}, {6}
   union(3,4): {0,1,2}, {3,4}, {5}, {6}
   union(5,6): {0,1,2}, {3,4}, {5,6}
   union(2,5): merges sets {0,1,2} and {5,6} into {0,1,2,5,6}, {3,4}

2. Step 2: Count remaining sets

   Remaining sets are {0,1,2,5,6} and {3,4}.
   Total distinct sets = 2.

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Count sets after unions = 2 [OK]

Hint: Count merged groups carefully after each union [OK]

Common Mistakes:

• Forgetting last union merges two sets
• Counting elements instead of sets
• Assuming all unions merge all sets