## 7 Discussion  
From the analysis of the obtained experimental results, we can assess that ESCAPE performs well in describing the six corpora under analysis, clustering the documents based on their main content. The proposed framework is generally able to group the documents into well-separated topics.  
We have observed that the joint-approach, which is based on a dimensionality algebraic phase before the application of the partitional K-means algorithms, is able to find homogeneous partitions in terms of documents for each cluster. In other words, this approach creates more balanced clusters. Moreover, changing the weighting strategy, the end-user is able to clusterize the same dataset at different granularity levels. Specifically, we have seen that the global weight IDF is able to create more clusters, and is also able to find sub-topics related to the major category. Thus, this weighting scheme is able to characterize each dataset in a more precise way. On the other hand, the entropy is able to find larger clusters, finding only the main relevant topics associated with each partition. Indeed, both the clusterizations are able to split the corpora into well-separated groups.  
For the probabilistic approach, considering the semantic similarity among the produced topics, it turned out that ESCAPE outperforms the currently used approach to find the proper number of clusters. As a matter of fact, the proposed algorithm is able to capture the effective cohesion level of the clusters, and then properly identify the optimal number of topics. The results obtained from all the datasets considered in the thesis confirm the clusters to be well-separated, especially for certain weighting schemes such as TF-IDF. Nevertheless, with respect to the joint-approach, some weighting schemes lead to very  
Figure 18: D1 t-SNE representation, Boolean-TF weighting scheme, \(K=5\), without post-processing (**left**) and with post-processing (**right**).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline \(K\) & **Topic Description** \\ \hline
**1** & game, team, sport, player, event, competition, ball, rule, international, must, country, united, man, national, run \\ \hline
**2** & space, theory, case, graph, define, function, note, every, write, order, result, element, must, system, general \\ \hline
**3** & music, musical, player, record, song, event, write, release, instrument, note, sound, international, style, piece, back \\ \hline
**4** & food, water, cooking, united, sometimes, produce, result, high, oil, modern, large, require, must, list, process \\ \hline
**5** & write, book, literature, story, character, art, university, music, novel, modern, english, word, note, study, later \\ \hline \hline \end{tabular}
\end{table}
Table 18: D1 topic–terms representation, Boolean-TF weighting scheme, \(K=5\), probabilistic modeling.  
poor results, such as the entropy-based scheme. In general, the probabilistic model tends to find more inhomogeneous clusters; however, despite these schemes, the other results are also satisfactory.  
ESCAPE turns out to be really helpful for analysts during their analytical tasks. Indeed, the analyst can choose to assign a different relevance to each word in the documents by means of different weights, and compare the solutions obtained using the two approaches, analyzing the different granularity levels. The best partitions can also be compared using innovative visualization techniques, which are able to help the analyst during the validation step. Moreover, the two proposed approaches are able to characterize different aspects in which the analyst may be interested, including also the possibility of comparing the proposed approaches with respect to the other state-of-the-art techniques.