  Industrial production processes, especially in the pharmaceutical industry,
are complex systems that require continuous monitoring to ensure efficiency,
product quality, and safety. This paper presents a hybrid unsupervised learning
strategy (HULS) for monitoring complex industrial processes. Addressing the
limitations of traditional Self-Organizing Maps (SOMs), especially in scenarios
with unbalanced data sets and highly correlated process variables, HULS
combines existing unsupervised learning techniques to address these challenges.
To evaluate the performance of the HULS concept, comparative experiments are
performed based on a laboratory batch
