  We present a new procedure to identify observations of known objects in large
data sets of unlinked detections. It begins with a Keplerian integrals method
that allows us to link two tracklets, computing preliminary orbits, even when
the tracklets are separated in time by a few years. In the second step, we
represent the results in a `graph' where the tracklets are the nodes and the
preliminary orbits are the edges. Then, acceptable `3-cycles' are identified
and a least squares orbit is computed for each of them. Finally, we construct
sequences of $n \geq 4$ tracklets by searching through the orbits of nearby
3-cycles and attempting to attribute the remaining tracklets. We calculate the
technique's efficiency at identifying unknown objects using real detections
that attempt to mimic key parameters of the Minor Planet Center's Isolated
Tracklet File (ITF) and then apply the procedure to the ITF to identify tens of
thousands of new objects.
