We present a comparison between two optical cluster finding methods: a matched filter algorithm using galaxy angular coordinates and magnitudes, and a percolation algorithm using also redshift information. We test the algorithms on two mock catalogues. The first mock catalogue is built by adding clusters to a Poissonian background, while the other is derived from N-body simulations. Choosing the physically most sensible parameters for each method, we carry out a detailed comparison and investigate advantages and limits of each algorithm, showing the possible biases on final results. We show that, combining the two methods, we are able to detect a large part of the structures, thus pointing out the need to search for clusters in different ways in order to build complete and unbiased samples of clusters, to be used for statistical and cosmological studies. In addition, our results show the importance of testing cluster finding algorithms on different kinds of mock catalogues to have a complete assessment of their behaviour.
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