Temporally Stable Clusters of Movie Series : A Machine Learning Approach to Content Segmentation

Sammanfattning: Clustering techniques have been shown to provide insight in various domains and applications. Adaptive evolutionary spectral clustering is a state-of-the-art method to obtain temporally stable clustering results from time-stamped data. This thesis explores the use of adaptive evolutionary spectral clustering to perform a clustering of film series into groups based on video streaming data. The developed method successfully performs a stable segmentation of film series into groups and introduces a number of extensions to the framework within the context of video on demand. We find that the implemented method allows for reasoning about clusters from an evolutionary perspective and that the state-of-the-art can be extended to introduce a dynamic number of clusters without negatively impacting the stability of properties of clusters.