A Semi-Supervised Framework of Clustering Selection for De-Duplication
Shrinu Kushagra, Hemant Saxena, Ihab F. Ilyas, Shai Ben-David · 2019
We view data de-duplication as a clustering problem. Recently, [1] introduced a framework called restricted correlation clustering (RCC) to model de-duplication problems. Given a set X, an unknown target clustering C* of X and a class F of clusterings of X, the goal is to find a clustering C from the set F which minimizes the correlation loss. The clustering algorithm is allowed to interact with a domain expert by asking whether a pair of records correspond to the same entity or not. Main drawback of the algorithm developed by [1] is that the pre-processing step had a time complexity of theta (|X|2) (where X is the input set). In this paper, we make the following contributions. We develop a sampling procedure (based on locality sensitive hashing) which requires a linear pre-processing time O(|X|). We prove that our sampling procedure can estimate the correlation loss of all clusterings in F using only a small number of labelled examples. In fact, the number of labelled examples is independent of |X| and depends only on the complexity of the class F. Further we show that to sample one pair, with high probability our procedure makes a constant number of queries to the domain expert. We then perform an extensive empirical evaluation of our approach which shows the efficiency of our method.