Given a set of records, a threshold value t and a similarity function, we investigate the problem of finding all pairs of records such that similarity between each pair is above t. We propose several optimizations on the existing approaches to solve the problem. Our algorithm outperforms the state-of-the-art algorithms in the case with large and high-dimensional datasets. The speedup we achieved varied from 30% to 4-x depending on the similarity threshold and the dataset properties.
|Title of host publication||Proceedings of the 12th International Asia-Pacific Web Conference (APWeb, Busan, Korea, April 6-8, 2010)|
|Publisher||IEEE Computer Society|
|Publication status||Published - 2010|