Towards Learning Cross-Modal Perception-Trace Models
Table 3: Silhouette Coefficient (SK) and Variance Ratio Criterion (VRC) of the compared models.
['Model', 'AP SK', 'AP VRC', 'HDBSCAN SK', 'HDBSCAN VRC']
To furthermore estimate the clustering validity in a quantitative manner, we computed the Silhouette Coefficient [rousseeuw1987silhouettes] and the Variance Ratio Criterion [calinski1974dendrite] for the clusters yielded by applying both algorithms. the results of the graph- and density-based cluster analysis performed on our model score higher on the both used coefficients. The value of the silhouette coefficient for the word2vec representations is close to zero which indicates overlapping clusters. Also the difference in the SK scores for the GloVe vectors and our model cannot be considered negligible and suggests that clusters identified using GloVe representations contain more misplaced objects. The variance ratio criterion is in contrast to the the silhouette coefficient not normalized to a fixed range, making a comparison of different models difficult. However, it is observable that for all models the value of the VRC for the results of HDBSCAN is generally lower than for the AP clustering. Comparing the GloVe representations and our model, we see a stronger decline in the index value for the GloVe vectors, which can imply a more robust clustering structure of our embeddings.