Sorting is the process of arranging data in a specific order, usually:

• Ascending order (small → large)

• Descending order (large → small)

Sorting improves searching efficiency, data organization, and overall performance of algorithms.

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 Classification of Sorting Techniques

Sorting algorithms are mainly classified into:

1️⃣ Internal Sorting
2️⃣ External Sorting

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1️⃣ Internal Sorting

• Entire data fits in main memory (RAM).

• Used for smaller datasets.

Common Internal Sorting Techniques

✔ Bubble Sort

• Repeatedly compares adjacent elements and swaps them if needed.

• Simple but inefficient for large data.

• Time Complexity: O(n²)

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✔ Selection Sort

• Selects the smallest element and places it in the correct position.

• Time Complexity: O(n²)

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✔ Insertion Sort

• Inserts each element into its proper position.

• Efficient for small or nearly sorted data.

• Time Complexity: O(n²)

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✔ Merge Sort

• Uses Divide and Conquer technique.

• Divides the array into halves, sorts them, and merges.

• Time Complexity: O(n log n)

• Stable sort.

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✔ Quick Sort

• Also uses Divide and Conquer.

• Selects a pivot and partitions the array.

• Average Time Complexity: O(n log n)

• Worst Case: O(n²)

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✔ Heap Sort

• Uses Heap data structure.

• Time Complexity: O(n log n)

• Not stable.

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2️⃣ External Sorting

• Used when data does not fit into main memory.

• Data is stored in external storage (disk).

✔ Example:

• External Merge Sort

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🔹 Comparison Table

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 Based on Method Used

1️⃣ Comparison-Based Sorting

• Bubble, Selection, Insertion, Merge, Quick, Heap

2️⃣ Non-Comparison Sorting

• Counting Sort

• Radix Sort

• Bucket Sort

(Time complexity can be better than O(n log n) under certain conditions.)

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 Conclusion

• For small datasets → Insertion or Bubble Sort

• For large datasets → Merge Sort or Quick Sort

• For memory-limited systems → Heap Sort

• For very large external data → External Merge Sort