Multidimensional projection techniques provide graphical representations computed based on instance similarities to enable the analysis of abstract and possibly large data sets. However, when the data set size grows these graphical representations can hardly avoid overlap among markers. To overcome this issue, while some techniques attempt to remove overlap after multidimensional projection, some projection techniques were developed considering non-overlapping constraints. In this work, we present an analysis of four overlap removal techniques and two projection techniques considering non-overlapping. The evaluation was performed according to five metrics that consider structural and similarity relations, and based on the results we provide a guide to use a technique according to the data set and analysis goals.
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