## 4 Experiment  
The methodology proposed in the previous section is implemented in R and is applied to the HCAHPS dataset of a hospital with 2652 records with the same features explained in Section 2.1. First, K-means++ is used to cluster all records based on the patients' responses to the satisfaction questions. A Self-organizing feature map is used to visualize data distribution in each cluster (Figure 2). The map shows that Kmeans++ divided data according to the [PERSON] (2015) criterion into three clusters based on their responses to the satisfaction questions. These clusters are shown in different colors in Figure 2. In the next step, the salient features of each cluster are extracted to identify the most important features which constitute a cluster. Extracted salient features were interpreted according to nature of each variable whether it is continuing, categorical, or dichotomous. Table 8 asserts the interpretation of the extracted salient features. For example, cluster 1 represent patients who were well-informed about their symptoms after leaving the hospital and expressed high satisfaction with help after discharge and high overall satisfaction. \begin{table}
\begin{tabular}{c c c} \hline _Cluster_ & \multicolumn{2}{c}{_Salient Features_} \\ \hline \multirow{2}{*}{_C1_} & Pain Management & Low \\  & Cleanliness & Low \\ \hline \multirow{2}{*}{_C2_} & Communication with Nurse & Low \\  & Quietness & Low \\ \hline \multirow{2}{*}{_C3_} & Communication with doctor & Low \\  & Pain Management & High \\ \multicolumn{2}{c}{} & Cleanliness & High \\ \hline \end{tabular}
\end{table} 
Table 7: Salient features. \begin{table}
\begin{tabular}{c c c} \hline _Cluster_ & \multicolumn{2}{c}{_Salient Features_} \\ \hline \multirow{3}{*}{_C1_} & \(\bullet\) & High Satisfaction with Help after discharge \\  & \(\bullet\) & High Satisfaction with Symptoms info \\  & \(\bullet\) & High Overall Health \\ \hline \multirow{3}{*}{_C2_} & \(\bullet\) & High Satisfaction with Help after discharge \\  & \(\bullet\) & High Satisfaction with Symptoms info \\ \hline \multirow{3}{*}{_C3_} & \(\bullet\) & Low Satisfaction with Help after discharge \\  & \(\bullet\) & Low Satisfaction with Symptoms info \\ \cline{1-1}  & \(\bullet\) & Low Overall Health \\ \hline \end{tabular}
\end{table} 
Table 8: Salient features of clusters in the first layer. Figure 2: The clusters produced based on patients’ responses to satisfaction questions. Although theoretically salient features are expected be different in different clusters, this expectation could be violated if the clusters are of significantly different sizes. For example, in Table 2, \(C1\), and \(C2\) share some features (i.e., high satisfaction with help after discharge and high satisfaction with symptoms info). This is due to the fact that \(C3\) is much bigger than \(C1\) and \(C2\) (Figure 1). Therefore, it has a significant effect on the value of difference factor, highlighting the shared salient features in \(C1\) and \(C2\). Although it might appear that \(C2\) is redundant, \(C1\) and \(C2\) were divided into two distinct clusters mainly because of their difference in the overall health feature. The records in each cluster are fed into the second layer of clustering. In this layer, patients are clustered based on their demographic data such as (e.g., age, sex, race, etc.). The first cluster is divided into four demographic sub-clusters and the second and third clusters are both divided into three demographic sub-clusters.