The ensemble-based evaluation protocol proposed in the study can be extended to other types of non-categorical datasets beyond 3D CAD models by adapting the methodology to suit the specific characteristics of the new dataset. Here are some ways to extend the protocol:
Data Representation: Ensure that the data representation used in the ensemble method aligns with the nature of the new dataset. For instance, if the dataset consists of textual data, appropriate embedding techniques can be employed to convert the text into numerical vectors for comparison.
Annotation Strategy: Develop a tailored annotation strategy that accounts for the unique features of the new dataset. This may involve defining specific similarity criteria relevant to the dataset and providing clear guidelines to annotators.
Ensemble Model Selection: Choose a diverse set of clustering algorithms or similarity metrics that are suitable for the characteristics of the new dataset. The ensemble should include methods that capture different aspects of similarity to provide a comprehensive evaluation.
Evaluation Metrics: Define evaluation metrics that are relevant to the specific dataset. Consider metrics that account for the inherent properties of the data and provide a holistic view of the clustering performance.
Consensus Building: Implement a robust consensus mechanism to combine the annotations or clustering results from multiple sources. This can help mitigate biases and improve the overall reliability of the evaluation.
By customizing the ensemble-based evaluation protocol to the specific requirements of different non-categorical datasets, researchers can effectively assess clustering algorithms and facilitate advancements in the field of deep learning for diverse data types.