Improving the Applicability of Variational Deep Embedding in Unsupervised Large-Scale Data Clustering

• Main Author: Zhu, Wenfei
• Format: Others
• Language: English
• Published: Uppsala universitet, Institutionen för informationsteknologi 2020
• Subjects: Engineering and Technology; Teknik och teknologier
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-439300

The purpose of the thesis is to apply deep clustering (DC) on King'splayer segmentation. To that end we propose six crucial properties a DCneeds to meet in the context of big data applicability. We implement our method based on VaDE (Variational Deep Embedding) together with four improvements to meet the six criteria, the method is called S3VaDE, a simple, stable and scalable VaDE. The experiments investigate the accuracy, stability and scalability between S3VaDE and VaDE on three benchmark datasets. The results show that S3VaDE out performed state-ofthe-art. In the thesis, we also demonstrate how to do model selection by visualizing latent space. We then apply S3VaDE on King's dataset and interpret the clusters with three KPIs, player engagement, skill leveland monetization. The analysis shows that the clusters are balanced and interpretable. The investigation further shows that the model is stableduring fine-tune.