Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
This work addresses evaluation challenges for researchers using deep clustering methods, but it is incremental as it builds on existing validation measures.
The paper tackles the problem of evaluating clustering quality in deep learning by identifying issues with traditional validation measures in high-dimensional spaces and proposing a systematic framework for applying them, which experiments show aligns better with external validation measures and reduces misguidance.
Deep clustering, a method for partitioning complex, high-dimensional data using deep neural networks, presents unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering, which involves projecting data into lower-dimensional embeddings before partitioning. Two key issues are identified: 1) the curse of dimensionality when applying these measures to raw data, and 2) the unreliable comparison of clustering results across different embedding spaces stemming from variations in training procedures and parameter settings in different clustering models. This paper addresses these challenges in evaluating clustering quality in deep learning. We present a theoretical framework to highlight ineffectiveness arising from using internal validation measures on raw and embedded data and propose a systematic approach to applying clustering validity indices in deep clustering contexts. Experiments show that this framework aligns better with external validation measures, effectively reducing the misguidance from the improper use of clustering validity indices in deep learning.