I'm programming a minhashing algorithm in Java that requires me to generate an arbitrary number of random hash functions (240 hash functions in my case), and run any number of integers through it (2000 at the moment).
In order to do that, I've been generating random numbers a, b, and c (from the range 1 - 2001) for each of the 240 hash functions. Then, my hash function returns h = ((a*x) + b) % c, where h is the return value and x is one of the integers run through it.
Is this an efficient implementation of random hashing, or is there a more common/acceptable way to do it?
This post was asking a similar question, but I'm still somewhat confused by the wording of the answer: Minhash implementation how to find hash functions for permutations
When I was working with Bloom filters a few years ago, I ran across an article that describes how to generate multiple hash functions very simply, with a minimum of code. The method he describes works very well. See Less Hashing, Same Performance: Building a Better Bloom Filter.
The basic idea is to create two hash functions, call them
h2, with which you can then simulate multiple hash functions,
gk, using the formula:
gi = h1(x) + i*h2(x)
i varies from 1 to
k (the number of hash functions you want).
The paper is well worth reading, even if you decide not to implement his idea. Although after reading it I can't imagine not wanting to implement it. It made my Bloom filter code a whole lot more tractable and didn't negatively impact performance.