## 4 Evaluation
### Results  
Table 1 shows a summary of the results obtained by our system and the baseline system when clustering the _Reuters-RCV1_ Corpus. When calculating recall and precision, for every reference cluster we identified the closest cluster produced by the system being evaluated. The results obtained by our system show a high recall and a very low precision. The clustering component produced some very large clusters containing a huge number of documents, and the remaining documents were scattered in very small clusters. The recall scores of some categories was 1, or nearly 1. In these cases the produced cluster that is closest to the reference cluster is a very large cluster that encompasses the entire or most of the reference cluster. Since all (or most) of the reference cluster's documents are in that large produced cluster, the recall is 1 (or nearly 1). However, since this closest cluster contains also a large number of documents that do not form part of the reference cluster, the resulting precision is very low. The F-measure, which in our case is the average between the recall and precision, is very low as a result of the very low precision values. The baseline clustering system produces better results albeit still with very low F-measure values. The incidence of very large clusters, though still present, is lower in the baseline system. Therefore the recall is lower and the precision is higher. The most probable reason for better results is that the baseline system knows the number of clusters to produce beforehand, and also performs cluster reorganisation. The results obtained by our system and the baseline system when clustering the _Yahoo! News_ Corpus are shown in Table 2.  
The results obtained when clustering the _Yahoo! News_ corpus, both by our system and also by the baseline system, are significantly better when compared to the results obtained when clustering the _Reuters-RCV1_ corpus. There are no more instances of full recall and minimal precision as before, and the F-measure values show a definite increase. There were a number of cases where the clusters produced by our system had a precision of 1 albeit with a lower recall. A produced cluster with a precision of 1 means that it does not contain any \"wrong\" documents in it. However, the fact that recall is lower than 1 means that the produced cluster does not contain all the documents in the corresponding reference cluster. Despite the improvement in results, the F-measure values are still quite low--0.2159 for our clustering system, and 0.3091 for the baseline system. \begin{table}
\begin{tabular}{l c c c} \hline  & **Recall** & **Precision** & **FM** \\ \hline Our System & 0.9076 & 0.0162 & 0.0230 \\ Baseline System & 0.4931 & 0.0872 & 0.0937 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of Clustering Results using _Reuters-RCV1_ Corpus. \begin{table}
\begin{tabular}{l c c c} \hline  & **Recall** & **Precision** & **FM** \\ \hline Our System & 0.3851 & 0.2690 & 0.2159 \\ Baseline System & 0.4387 & 0.3018 & 0.3091 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of Clustering Results using _Yahoo! News_ Corpus. Table 3 presents a summary of the evaluation results obtained when the _Google News_ corpus was clustered by three systems: our system; a modification of our system where each document is placed within the most similar cluster; and the baseline system. When viewing Table 3, one can notice that the results obtained by our clustering system when clustering the _Google News_ corpus are very good compared to the results obtained by this same system when clustering the _Reuters-RCV1_ corpus and _Yahoo! News_ corpus. There were many cases where both the recall and precision scores are 1. This means that in these cases, the produced clusters were identical to the reference clusters. There are very few cases where the recall or precision are low. The average F-measure is 0.8019--a huge improvement on the F-measures obtained when clustering the other two corpora (0.2159 for the _Yahoo!