Effective and Fast Near Duplicate Detection via Signature-Based Compression Metrics

Detecting near duplicates on the web is challenging due to its volume and variety. Most of the previous studies require the setting of input parameters, making it difficult for them to achieve robustness across various scenarios without careful tuning. Recently, a universal and parameter-free similarity metric, the normalized compression distance or NCD, has been employed effectively in diverse applications. Nevertheless, there are problems preventing NCD from being applied to medium-to-large datasets as it lacks efficiency and tends to get skewed by large object size. To make this parameter-free method feasible on a large corpus of web documents, we propose a new method called SigNCD which measures NCD based on lightweight signatures instead of full documents, leading to improved efficiency and stability. We derive various lower bounds of NCD and propose pruning policies to further reduce computational complexity. We evaluate SigNCD on both English and Chinese datasets and show an increase in score compared with the original NCD method and a significant reduction in runtime. Comparisons with other competitive methods also demonstrate the superiority of our method. Moreover, no parameter tuning is required in SigNCD, except a similarity threshold.