## 4 Experiments
### Sentences as Only Input Data
#### 4.2.2 Dimensionality Reduction Techniques Variation  
The process of converting raw text into word vectors used in clustering techniques produces vectors of variable size. Depending on the size of input data and the word embedding technique applied, the output vectors can reach a size in the order of the hundreds or even thousands per sentence, a length of 200 for the case of GloVe, and a length of 32,768 when we applied SBERT. This high number of features per sentence requires very high processing conditions in terms of RAM memory and processing cores, which are sometimes impossible to have, becoming computationally inefficient generally. In clustering, this is intensified because it makes it harder for a clustering technique tofind similarities in the data to cluster them together when this vast number of features are supplied as input. Dimensionality reduction techniques, as stated in Section 3.2.2, enhance the methodology's computational efficiency and improve the clustering techniques' performance. Despite the known benefits of dimension reduction techniques, some disadvantages may come with their application. For instance, reducing features may lead to information loss. In addition to that, additional effort is needed to run several tests to find the optimal value for dimensionality reduction. Having decided on word embeddings to be used in the experiments, we chose not to fixate the number of clusters (K) and test it out with multiple K ranging from 2 to 128 in exponentials of 2. With this change, our ground labels could not be used, since the number of clusters might not be the same as the number of unique ground labels, leading to only the silhouette score getting used. The combination of the silhouette score and the K value it makes out for each approach will dictate the quality of the results. Figure 3 shows the best silhouette score for each technique without applying dimensionality reduction techniques. In contrast, Figure 4 shows how the clustering results improve when putting all the embedders with the exact final dimensions, which are 200 from the GloVe word embedder technique. We can see a tendency where UMAP outperforms PCA in this number of dimensions. Figure 5 shows how the silhouette score varies when reducing the number of dimensions of the embeddings; analyzing the tendencies of the approaches, since multiple approaches are overlapping, making it less readable, we can see that most approaches presented there show a minimal increase in performance until ten dimensions, where it starts to steadily increase as dimensions get reduced, except the agglomerative hierarchical clustering technique with GloVe word embedder and PCA dimensionality reduction that shows a stabilization until 15 dimensions and then a decrease in performance and joining the tendency of the remaining approaches on that graph after ten dimensions. The same pattern can be seen in Figure 6 with the TFIDF with PCA, which spiked in performance after ten dimensions. The GloVe with UMAP does not have a perceivable pattern where it spikes at specific dimensions. Based on most approach patterns, a decision was made to start experimenting from only ten until one dimension. Figure 2: Kmeans performance with different word embedders on the baseball dataset. Figure 4: Clustering results with every approach at the 200 dimensions on the baseball dataset. Figure 5: Clustering tendencies through dimension reduction on the baseball dataset. Figure 3: Clustering results in the new test settings on the baseball dataset. Even though it displays promising results silhouette score-wise, when analyzing the number of clusters used to reach those values, they either maximize at the 128 clusters (Figure 7A) or on 2 clusters (Figure 7B), which is not the expected result for this task, since 2 clusters group the data with no perceivable differences and 128 clusters are too many clusters with no interest for the solution of the problem that this works intends to solve.