DeepECT: The Deep Embedded Cluster Tree
- Author(s)
- Dominik Mautz, Claudia Plant, Christian Böhm
- Abstract
The idea of combining the high representational power of deep learning techniques with clustering methods has gained much attention in recent years. Optimizing a clustering objective and the dataset representation simultaneously has been shown to be advantageous over separately optimizing them. So far, however, all proposed methods have been using a flat clustering strategy, with the actual number of clusters known a priori. In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method. The cluster tree does not need to know the actual number of clusters during optimization. Instead, the level of detail to be analyzed can be chosen afterward and for each sub-tree separately. An optional data-augmentation-based extension allows DeepECT to ignore prior-known invariances of the dataset, such as affine transformations in image data. We evaluate and show the advantages of DeepECT in extensive experiments.
- Organisation(s)
- Research Network Data Science, Research Group Data Mining and Machine Learning
- External organisation(s)
- Ludwig-Maximilians-Universität München
- Journal
- Data Science and Engineering
- Volume
- 5
- Pages
- 419-432
- No. of pages
- 14
- ISSN
- 2364-1185
- DOI
- https://doi.org/10.1007/s41019-020-00134-0
- Publication date
- 12-2020
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102033 Data mining
- Keywords
- ASJC Scopus subject areas
- Computer Science Applications, Computational Mechanics
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/deepect-the-deep-embedded-cluster-tree(c175a5ff-ad08-4fa6-bd59-0dbfc116b458).html