  A novel combination of data analysis techniques is proposed for the
reconstruction of all tracks of primary charged particles, as well as of
daughters of displaced vertices (decays, photon conversions, nuclear
interactions), created in high energy collisions. Instead of performing a
classical trajectory building or an image transformation, an efficient use of
both local and global information is undertaken while keeping competing choices
open. The measured hits of adjacent tracking layers are clustered first with
the help of a mutual nearest neighbor search in the angular distance. The
resulted chains of connected hits are used as initial clusters and as input for
cluster analysis algorithms, such as the robust $k$-medians clustering. This
latter proceeds by alternating between the hit-to-track assignment and the
track-fit update steps, until convergence. The calculation of the hit-to-track
distance and that of the track-fit $\chi^2$ is performed through the global
covariance of the measured hits. The clustering is complemented with elements
from a more sophisticated Metropolis-Hastings MCMC algorithm, with the
possibility of adding new track hypotheses or removing unnecessary ones.
Simplified but realistic models of today's silicon trackers, including the
relevant physics processes, are employed to test and study the performance
(efficiency, purity) of the proposed method as a function of the particle
multiplicity in the collision event.
