Context. A minority of criminals performs a majority of the crimes today. It is known that every criminal or group of offenders to some extent have a particular pattern (modus operandi) how crime is performed. Therefore, computers' computational power can be employed to discover crimes that have the same model and possibly are carried out by the same criminal. The goal of this thesis was to apply the existing Series Finder algorithm to a feature-rich dataset containing data about Swedish residential burglaries.

Objectives. The following objectives were achieved to complete this thesis: Modifications performed on an existing Series Finder implementation to fit the Swedish police forces dataset and MatLab code converted to Python. Furthermore, experiment setup designed with appropriate metrics and statistical tests. Finally, modified Series Finder implementation's evaluation performed against both Spatial-Temporal and Random models.

Methods. The experimental methodology was chosen in order to achieve the objectives. An initial experiment was performed to find right parameters to use for main experiments. Afterward, a proper investigation with dependent and independent variables was conducted.

Results. After the metrics calculations and the statistical tests applications, the accurate picture revealed how each model performed. Series Finder showed better performance than a Random model. However, it had lower performance than the Spatial-Temporal model. The possible causes of one model performing better than another are discussed in analysis and discussion section.

Conclusions. After completing objectives and answering research questions, it could be clearly seen how the Series Finder implementation performed against other models. Despite its low performance, Series Finder still showed potential, as presented in future work.