Abstract

In this paper, we include an in-depth analysis of DL-based research clustering approaches. Cluster findings in the three separate use cases representing different data types reveal that DL-based clustering algorithms exceed these approaches. Overall assessments are however delayed because of the small number of marked data for GE-bio-imaging and clustering. Neural networks usually need a great deal of samples for generalization.