  In this paper, we present the tools used to search for galaxy clusters in the
Kilo Degree Survey (KiDS), and our first results. The cluster detection is
based on an implementation of the optimal filtering technique that enables us
to identify clusters as over-densities in the distribution of galaxies using
their positions on the sky, magnitudes, and photometric redshifts. The
contamination and completeness of the cluster catalog are derived using mock
catalogs based on the data themselves. The optimal signal to noise threshold
for the cluster detection is obtained by randomizing the galaxy positions and
selecting the value that produces a contamination of less than 20%. Starting
from a subset of clusters detected with high significance at low redshifts, we
shift them to higher redshifts to estimate the completeness as a function of
redshift: the average completeness is ~ 85%. An estimate of the mass of the
clusters is derived using the richness as a proxy. We obtained 1858 candidate
clusters with redshift 0 < z_c < 0.7 and mass 13.5 < log(M500/Msun) < 15 in an
area of 114 sq. degrees (KiDS ESO-DR2). A comparison with publicly available
Sloan Digital Sky Survey (SDSS)-based cluster catalogs shows that we match more
than 50% of the clusters (77% in the case of the redMaPPer catalog). We also
cross-matched our cluster catalog with the Abell clusters, and clusters found
by XMM and in the Planck-SZ survey; however, only a small number of them lie
inside the KiDS area currently available.
