The Rabin-Karp algorithm is a string matching algorithm that uses hashing techniques to find occurrences of a pattern within a larger text.

Algorithm:

1. Preprocess the pattern:

- Calculate the hash value of the pattern.

- Calculate the hash value of the first substring in the text with the same length as the pattern.

2. Slide the pattern over the text:

- Compare the hash value of the current substring in the text with the hash value of the pattern.

- If the hash values match, perform comparison of each character to confirm the match.

- If the hash values do not match, move the pattern one position to the right and compute the hash value of the new substring.

3. Repeat step 2 until either a match is found or the end of the text is reached.

4. If a match is found, record the position of the match or perform any desired action.

5. If the end of the text is reached without finding a match, then the pattern does not exist in the text.

The Rabin-Karp algorithm achieves an average-case time complexity of O(n + m), where n is the length of the text and m is the length of the pattern.

The hashing technique allows for faster comparisons by quickly identifying potential matches based on the hash values. However, it's important to handle potential hash collisions to ensure accurate results.

Selecting Hash Function:

When implementing the Rabin-Karp algorithm, selecting an appropriate hash function is important to ensure accurate and efficient pattern matching.

Following things can be considered while choosing a hash function:

1. Hash Function Properties: The hash function should distribute the hash values uniformly across the entire range to minimize collisions. The hash function should be efficient to compute hash values quickly. Hence the function should be uniform as well as efficient.

2. Rolling Hash Function: Since the Rabin-Karp algorithm involves sliding the pattern over the text, a rolling hash function is often used. It allows for efficient updates to the hash value when shifting the pattern by one position.

3. Hash Collisions: Choosing a hash function with a low collision rate is crucial for the accuracy of the algorithm.