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Stars form within the dense cores of molecular clouds. Thus, a deep understanding of the process of star formation requires the study of molecular cloud structure, the localization of the embedded clumps and the determination of their physical and kinematic properties. To pursue this in molecular cloud complexes, one needs to rely on automatic software, so it is extremely important to test the validity of these
procedures. In this work, we present a comparison of different methods applied to the data set collected by the COMPLETE team in the Perseus Molecular Cloud Complex. In particular, clump-finding algorithms with different input parameters have been run with the aim of quantifying the robustness of the method as well as the main differences and biases. Surprisingly, even using the same algorithm, slightly different initial parameters lead to significantly different outputs. We compare the results and discuss how small parameter changes affect: masses and sizes, the derived mass distribution function, and the virial parameter. Finally, we suggest how to choose the best parameters to use in the algorithms.
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