SimHash algorithm, introduced by Charikar and is patented by Google.
tokenize
tokenize your data, assign weights to each token, weights and tokenize function are depend on your business
hash (md5, SHA1)
calculate token‘s hash value and convert it to binary (101011 )
weigh values
for each hash value, do hash*w, in this way: (101011 ) -> (w,-w,w,-w,w,w)
merge
add up tokens‘ values, to merge to 1 hash, for example, merge (4 -4 -4 4 -4 4) and (5 -5 5 -5 5 5) , results to (4+5 -4+-5 -4+5 4+-5 -4+5 4+5)，which is (9 -9 1 -1 1)
Dimensionality Reduction
Finally, signs of elements of V corresponds to the bits of the final fingerprint, for example (9 -9 1 -1 1) -> (1 0 1 0 1), we get 10101 as the fingerprint.
Hamming distance can be used to find the similarity between two given data, calculate the Hamming distance between 2 fingerprints.
Based on my experience, for 64 bit SimHash values, with elaborate weight values, distance of similar data
often differ appreciably in magnitude from those unsimilar data.
how to calculate：XOR, 只有两个位不同时结果是1 ，否则为0，两个二进制value“异或”后得到1的个数 为海明距离 。
simhash 0.1.0 : Python Package Index
[SimHash] find the percentage of similarity between two given data
原文：http://www.cnblogs.com/scottgu/p/5542184.html