  We present a new cluster detection algorithm designed for finding
high-redshift clusters using optical/infrared imaging data. The algorithm has
two main characteristics. First, it utilises each galaxy's full redshift
probability function, instead of an estimate of the photometric redshift based
on the peak of the probability function and an associated Gaussian error.
Second, it identifies cluster candidates through cross-checking the results of
two substantially different selection techniques (the name 2TecX representing
the cross-check of the two techniques). These are adaptations of the Voronoi
Tesselations and Friends-Of-Friends methods. Monte-Carlo simulations of mock
catalogues show that cross-checking the cluster candidates found by the two
techniques significantly reduces the detection of spurious sources.
Furthermore, we examine the selection effects and relative strengths and
weaknesses of either method. The simulations also allow us to fine-tune the
algorithm's parameters, and define completeness and mass limit as a function of
redshift. We demonstrate that the algorithm isolates high-redshift clusters at
a high level of efficiency and low contamination.
