## 4 Experiment
The process is outlined in Algorithm 3 and the outcome of this step is visualized in Figure 3.  
```
Find Recommendations (data, split_index)  Satisfaction_questions_data \(\leftarrow\) data [:] [0:split_index] \(\text{noc}\leftarrow\text{FindNumberOfClusters}\) (Satisfaction_questions_data ) \(\text{Sq\_clustered\_data}=\text{kmeans}\) (Satisfaction_questions_data) \(\text{Sq\_salient\_dim}\leftarrow\text{FindingSalientFeatures}\) (noc, \(\text{Sq\_clustered\_data}\), \(\text{z}\)) For i \(\leftarrow\) 0 to noc  For j in \(\text{Sq\_clustered\_data}[i]\)  Demographic_data \(\leftarrow\) data[j] [split_index: number_of_columns(data)] \(\text{D\_clustered\_data}\leftarrow\text{kmeans}\) (Demographic_data) \(\text{D\_salient\_dim}\leftarrow\text{FindingSalientFeatures}\) (noc, \(\text{D\_clustered\_data}\), \(\text{z}\)) For sq_salient in \(\text{Sq\_salient\_dim}[i]\):  If sq_salient!= 0:  For d_salient in \(\text{D\_salient\_dim}\):  If d_salient!= 0:  Recommendation [(col_name(sq_salient), sq_salient)]. Add ((col_name(d_salient), d_salient)) Return recommendation
```  
**Algorithm 3**Recommendation extraction. Once again salient feature extraction algorithm is applied on each sub-cluster. The results are asserted in Table 9.  
Figure 3: Sub-clusters produced based on patient demographic data (**a**) demographic sub-clusters of the first satisfaction cluster, (**b**)demographic sub-clusters of the second satisfaction cluster, (**c**) demographic subclusters of the third satisfaction cluster. Putting the salient features of a satisfaction cluster and the salient features of its demographic sub-cluster, we derived the associations listed in Table 10. These associations represent a possible source of significant satisfaction questions. For instance, the source of low satisfaction with help after discharge according to _C3_ is communication with old people referred from a physician or Spanish speaking patients admitted from the emergency room. Transforming these associations into recommendations can offer hospital policy changes in favor of both patients and hospitals. The proper recommendation in this case can be putting more effort in communication with elderly people and Spanish people, or hiring a Spanish speaking nurse or interpreter. It is worth noting, that the method explained in this section, works better in terms of performance and accuracy than in methods such as association rule mining, which has its well-known limitations including a brute-force method for extracting association rules and the risk of finding many irrelevant rules. \begin{table}
\begin{tabular}{c l l l l l} \hline \hline \multicolumn{1}{c}{1} & \multicolumn{2}{l}{Patients who have high Satisfaction with Help After Discharge, have these qualities:} \\  & & Mostly Physician Referral Admission Source, Mostly not Medical Principal Reason of Admission & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \multirow{2}{*}{2} & \multicolumn{2}{l}{Patients who have high Satisfaction with Symptoms Info, have these qualities:} \\  & & Mostly Physician Referral Admission Source, Rarely Medical Principal Reason of Admission & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \multirow{2}{*}{3} & \multicolumn{2}{l}{Patients who have high Satisfaction with Help After Discharge, have these qualities:} \\  & & Mostly Physician Referral Admission Source & Rarely Medical Principal Reason of Admission & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \multirow{2}{*}{4} & \multicolumn{2}{l}{Patients who have high Satisfaction with Help After Discharge, have these qualities:} \\  & & Mostly Physician Referral Admission Source, Rarely Medical Principal Reason of Admission & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Derived Associations.