Cluster-Based Sorted Neighborhood for Efficient Duplicate Detection

Duplicate detection intends to find multiple and syntactically different representations of the same real-world entities in a dataset. The naive way of duplicate detection entails a quadratic number of pair-wise record comparisons to identify the duplicates. This number of comparisons might take hours even for an average sized dataset. As today's databases grow very fast, different candidate-selection methods, such as sorted neighborhood, blocking, canopy clustering and their variations, address this problem by shrinking the comparison space. The volume and velocity of data-change require ever faster and more flexible methods of duplicate detection. In particular, they need dynamic indices that can be updated efficiently as new data arrives. We present a novel approach, which combines the idea of cluster-based methods with the well-known sorted neighborhood method. It carefully filters out irrelevant candidate pairs, which are less likely to yield duplicates, by pre-clustering records based not only on their proximity after sorting, but also on their similarity in selected attributes. An empirical evaluation on synthetic and real-world datasets shows that our approach improves the overall runtime over existing approaches while maintaining comparable result quality. Meanwhile, it uses a dynamic indices, that in turns make it useful for deduplicating streaming data.