## 4 Evaluation
### Discussion of Results  
When analysing the results obtained from the evaluation of our clustering system, one can notice that our system attains very poor results when clustering the _Reuters-RCV1_ corpus. On the other hand, it achieves very good results when clustering the _Google News_ corpus. When clustering the _Yahoo! News_ corpus, it obtains results that are comparable (albeit poorer) to the ones obtained by the baseline system. The difference in the results when processing these three corpora is due to the difference in the \"entropy\" of the data corpora. In a data corpus with high entropy (such as _Reuters-RCV1_ corpus), there is no clear similarity threshold which can be used to classify the documents into different clusters. When we calculated the similarity of the documents in the _Reuters-RCV1_ corpus to their respective clusters, we found that these range from approximately 0.02 to near 0.55 with an average of less than 0.1. On the other hand, when we calculated the similarity of _Reuters-RCV1_ clusters with documents that are _not_ their members (we refer to this similarity as \"dis-similarities\"), we found that sometimes it even exceeded 0.3--this is significantly higher than the average similarity of the clusters with their respective \begin{table}
\begin{tabular}{l c c c} \hline  & **Recall** & **Precision** & **FM** \\ \hline Our System & 0.7737 & 0.9120 & 0.8019 \\ Modified System & 0.7931 & 0.9518 & 0.8370 \\ Baseline System & 0.7689 & 0.5935 & 0.6188 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of Clustering Results using _Google News_ Corpus. documents. Due to this overlap between these \"similarities\" and \"dis-similarities\", one cannot establish a clear similarity threshold that would warrant membership in the respective cluster. As a result of this our system obtains poor results. One should keep in mind that the similarity threshold has a crucial importance in our clustering system since it determines when new clusters should be created. On the other hand, the baseline system has the number of clusters provided beforehand and so does not has the problem of creating too many clusters. The problem of high entropy is also present, albeit to a lower extent, in the _Yahoo! News_ corpus. In this corpus, there is still a significant overlap for similarities between clusters and their constituent documents, and between clusters and non-member documents. However, since this overlap is less than in the case of the _Reuters-RCV1_ corpus, a similarity threshold can be selected that can be reasonably effective. Consequently, when clustering the _Yahoo! News_ corpus, our clustering system can achieve significantly better results than when clustering the _Reuters-RCV1_ corpus. However, the results obtained are still lower than the baseline, and therefore, one may conclude that our clustering component is not optimal for the clustering of documents into general categories. In comparison to the previous two cases, our clustering approach obtains significantly better results than the baseline system when clustering the _Google News_ corpus. The reason behind this is that apart from exceptional cases, the similarity between a cluster and a non-member document never exceeds the minimum similarity between a cluster and its member documents. When clustering the _Google News_ corpus, our _Document Clustering_ component achieves an average recall of 0.7737 and average precision of 0.9120. On the other hand, the baseline system obtains an average recall of 0.7689 and average precision of 0.5935. As one can note, there is a significant difference between these two results. The results obtained show that our clustering system is very effective in clustering news reports into event-centric clusters. However, its use for the classification of documents or news reports into more general clusters is not recommended.