Beyond decision graphs, other techniques that could be explored to eliminate input parameters in clustering algorithms for missing datasets or complex data scenarios include:
AutoML Approaches: Leveraging automated machine learning (AutoML) techniques to automatically select the optimal parameters for clustering algorithms based on the characteristics of the dataset. This can involve hyperparameter tuning, model selection, and feature engineering to optimize clustering performance.
Meta-Learning: Utilizing meta-learning frameworks to learn the best parameter settings for clustering algorithms across different datasets. By training a meta-learner on a diverse set of datasets, the algorithm can adapt and generalize well to new datasets without the need for manual parameter tuning.
Ensemble Methods: Employing ensemble methods to combine multiple clustering algorithms with different parameter settings. By aggregating the results of individual algorithms, ensemble methods can mitigate the impact of parameter sensitivity and improve overall clustering accuracy.
Deep Learning: Exploring deep learning models, such as neural networks, for clustering tasks with missing data. Deep learning architectures can learn complex patterns and relationships in the data, potentially reducing the reliance on manual parameter tuning and improving clustering performance in challenging scenarios.