Improving the Applicability of Variational Deep Embedding in Unsupervised Large-Scale Data Clustering
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 ourmethod based on VaDE (Variational Deep Embedding) together with fourimprovements to...
Main Author: | Zhu, Wenfei |
---|---|
Format: | Others |
Language: | English |
Published: |
Uppsala universitet, Institutionen för informationsteknologi
2020
|
Subjects: | |
Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-428739 |
Similar Items
-
Improving the Applicability of Variational Deep Embedding in Unsupervised Large-Scale Data Clustering
by: Zhu, Wenfei
Published: (2020) -
Intelligent Resource Management for Large-scale Data Stream Processing
by: Stein, Oliver
Published: (2019) -
Unsupervised text clusteringusing survey answers
by: Helgesson Törnqvist, Mathias, et al.
Published: (2017) -
Implementing and Evaluating Clustering Methods for Large Probabilistic Graphs
by: Nilsson, Alfred
Published: (2021) -
CONSTRUCTING AND VARYING DATA MODELS FOR UNSUPERVISED ANOMALY DETECTION ON LOG DATAData modelling and domain knowledge’s impact on anomaly detection and explainability
by: Vidmark, Anton
Published: (2019)