Motivating Clustering

A typical entity resolution pipeline starts with preprocessing records $\tilde{r}_1=C(r_1),\ldots,\tilde r_n=C(r_n)$ individually. Next comes pairwise feature engineering $s_{ij}=F(\tilde{r}_i,\tilde{r}_j)$, followed by pairwise matching $c_{ij}=M(s_{ij})$, where $c=1$ represents a match and $c=0$ otherwise—a binary classification problem.

Collective entity resolution goes beyond pairs to improve outcomes ...