Entity Resolution

Entity resolution (ER) is the problem of matching records that represent the same real-world entity and then merging the matching records. ER is a well known problem that arises in many applications. An exhaustive ER process involves comparing all the pairs of records, which can be very expensive for large datasets.

Parameters

$n$ : number of records
$k$ : number of features

Filters

Computational Model

Randomization

Approximation

Algorithms Table

Displaying 7 of 7 algorithms


Bellare Active Learning	2012	$O(n^2 \log n c \log c)$
Gupta & Sarawagi CRF	2009	$O(n^3 k)$	$O(nk)$
Ravikumar & Cohen Generative Models	2004	$O(n^2 k)$	$O(k)$
Ananthakrishna	2002	$O(n^2 k)$	$O(n)$
EM Based Winkler	2000	$O(n^3 k)$	$O(k)$
BOYS algorithm	1993	$O(n^2 k)$	$O(n^2)$
Fellegi & Sunter Model	1969	$O(n^3 k)$	$O(k)$

Reductions Table

Insuffient Data to display table

Other relevant algorithms

Displaying 1 of 1 other relevant algorithms