Huffman coding is a popular algorithm used for data compression. It is a variable-length prefix coding algorithm that assigns shorter codes to more frequently occurring characters and longer codes to less frequently occurring characters. The main goal of the algorithm is to minimize the total number of bits required to encode the given data.

To determine the time complexity of the Huffman coding algorithm, let's analyze its steps:

1. Frequency Calculation:
- In this step, the algorithm calculates the frequency of each character in the input data. This can be done in O(N) time, where N is the size of the input data.

2. Priority Queue Construction:
- The algorithm constructs a priority queue or a min-heap based on the character frequencies. This step takes O(N) time to insert all the characters into the priority queue.

3. Tree Construction:
- The algorithm repeatedly takes the two characters with the lowest frequencies from the priority queue, combines them into a new node, and inserts it back into the priority queue. This step continues until only one node remains in the priority queue, which represents the root of the Huffman tree.
- Each insertion operation in the priority queue takes O(logN) time, and there are N-1 such insertions, resulting in a total time complexity of O(NlogN) for tree construction.

4. Code Generation:
- Once the Huffman tree is constructed, the algorithm traverses the tree to generate the binary codes for each character. The code generation process takes O(N) time, as each character needs to be assigned a code based on its position in the tree.

Therefore, the overall time complexity of the Huffman coding algorithm is O(N) + O(N) + O(NlogN) + O(N), which simplifies to O(NlogN). This complexity arises from the construction of the priority queue and the Huffman tree.

In conclusion, the time complexity of the Huffman coding algorithm is O(NlogN), where N is the size of the input data.