diff --git a/reports/bolsonaro.tex b/reports/bolsonaro.tex
index 04b5827f7dbd962e89f69496aab04e46fcec0da5..9c83998387002b4b0eb4f112c02b0fbf3f596db0 100644
--- a/reports/bolsonaro.tex
+++ b/reports/bolsonaro.tex
@@ -75,6 +75,8 @@ where: $cor_{t_i, t_j} = correltion(predict_{t_i}, predict_{t_j} ) $ is the corr
 \item $measure_3$ 
 \end{itemize}
 For the experiments, they use breast cancer prognosis. They reduce the size of a forest of 100 trees to a forest of on average 26 trees keeping the same error rate.
+
+\item \cite{Fawagreh2015}: The goal is to get a much smaller forest while staying accurate and diverse. To do so, they used a clustering algorithm. Let $C(t_i, T) = \{c_{i1}, \dots, c_{im}\}$ denotes a vector of class labels obtained after having $t_i$ classify the training set $T$ of size $m$, with $t_i \in F$, $F$ the forest of size $n$. Let $\mathcal{C} = \bigcup^n_{i=1} C(t_i, T)$ be the super vector of all class vectors classified by each tree $t_i$. They then applied a clustering algorithm on $\mathcal{C}$ to find $k = \sqrt{\frac{n}{2}}$ clusters. Finally, the final forest $F'$ is composed on the union of each tree that is the most representative per cluster, for each cluster. So if you have 100 trees and 7 clusters, the final number of trees will be 7. They obtained at least similar performances as with regular RF algorithm.
 \end{itemize}
 
  
diff --git a/reports/bolsonaro_biblio.bib b/reports/bolsonaro_biblio.bib
index b4e16837e8ac4cbf97f251cd6b95e14186e83d78..94efbec8bd91feede5585d33eadb63f1b84e6e40 100644
--- a/reports/bolsonaro_biblio.bib
+++ b/reports/bolsonaro_biblio.bib
@@ -11,4 +11,20 @@
 @article{Zhang,
   title={Search for the smallest random forest},
   author={Zhang, Heping and Wang, Minghui}
-}
\ No newline at end of file
+}
+
+@article{Fawagreh2015,
+       author = {{Fawagreh}, Khaled and {Medhat Gaber}, Mohamad and {Elyan}, Eyad},
+        title = "{On Extreme Pruning of Random Forest Ensembles for Real-time Predictive Applications}",
+      journal = {arXiv e-prints},
+     keywords = {Computer Science - Machine Learning},
+         year = "2015",
+        month = "Mar",
+          eid = {arXiv:1503.04996},
+        pages = {arXiv:1503.04996},
+archivePrefix = {arXiv},
+       eprint = {1503.04996},
+ primaryClass = {cs.LG},
+       adsurl = {https://ui.adsabs.harvard.edu/abs/2015arXiv150304996F},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
+}