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Matching Entities from Multiple Sources with Hierarchical Agglomerative Clustering

: We propose extensions to Hierarchical Agglomerative Clustering (HAC) to match and cluster entities from multiple sources that can be either duplicate-free or dirty. The proposed scheme is comparatively evaluated against standard HAC as well as other entity clustering approaches concerning efﬁciency and efﬁcacy criteria. All proposed algorithms can be run in parallel on a distributed cluster to improve scalability to large data volumes. The evaluation with diverse datasets shows that the new approach can utilize duplicate-free sources and achieves better match quality than previous methods.