DeepECT: The Deep Embedded Cluster Tree

DeepECT: The Deep Embedded Cluster Tree

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.

Grafik Top
Authors
  • Mautz, Dominik
  • Plant, Claudia
  • Böhm, Christian
Grafik Top
Shortfacts
Category
Journal Paper
Divisions
Data Mining and Machine Learning
Journal or Publication Title
Data Science and Engineering
ISSN
23748486
Page Range
pp. 419-432
Number
4
Volume
5
Date
1 December 2020
Official URL
https://doi.org/10.1007/s41019-020-00134-0
Export
Grafik Top