# An Automated Design Method for Plane Trusses Based on User Preference Information
## 3 User Preference Prediction Model
### Strategies for Reducing User Fatigue  
Getting the evaluation information of user preference is an interactive process. It is well-known that user fatigue is a huge obstacle to obtaining effective information during this evaluation process. To solve such a problem, two measures are taken, i.e., limiting the number of interactions and reducing the interaction complexity. This section focuses on the former measure and the latter one will be introduced in Section 3.4. Previous studies have demonstrated that users prefer to group the samples based on their similarity before evaluation [36; 37]. Therefore, the user evaluation method \"choose first, evaluate later\" is proposed. This means that the users first choose a preferred group of samples. Then, they only evaluate the samples in this group. This effectively reduces the number of interactions. Thus, it is vital to find an appropriate clustering method \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Basic Size** & **Complexity** & \begin{tabular}{c} **Mid-Span** \\ **Width** \\ \end{tabular} & **Flatness** & **Symmetry** \\ \hline Height-Span & Number of & Truss Depth & \
\begin{tabular}{c} Top Chord \\ Height \\ Differences \\ \end{tabular} & Symmetry Rate \\ Mid-span Clear & Average Length & Dispersion of &
\begin{tabular}{c} Bottom Chord \\ Height \\ Differences \\ \end{tabular} & \\ Height & of Web Members & Nodes & Differences & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature vector. to group the truss sample population before the user evaluation. This study selects six specific clustering methods as candidates, including K-means, DBSCAN [38], CLIQUE [39], K-medoids [40], SOM [41], and CURE [42] methods. These methods are evaluated from two perspectives, including subjective consistency and numerical performance. To detect the consistency of the clustering results with user judgment, a subjective evaluation experiment is conducted. Seven students of civil engineering or computer science were invited to compare the truss samples in pairs and rate them based on their similarities. The score varies from 1 to 5. A value of 5 means that the two samples are highly similar, whereas 1 means that they are completely different. The truss samples are randomly selected from the sample population. In total, five random groups (numbered from 1 to 5) are generated, each of which contains 20 samples. Each student needs to evaluate all the groups, in which the samples are presented in the form of pictures, as shown in Figure 4. The final score of each sample pair is calculated from the average of the seven students' scores. After the experiment, the candidate methods are used to cluster the truss samples of each random group. A rating rule is presented to evaluate the clustering results according to the experimental results. Assigning a similar sample pair with a higher score to the same group will result in a better reward, e.g., a clustering method will get 5 points if it assigns a 5-score sample pair to the same group; otherwise it will lose 5 points. Based on this rule, the grade of each candidate method can be calculated. The higher the grade, the better the performance. The evaluation results are summarized in Table 2. The SOM method achieves the highest grade in groups 1, 3, 4, and 5, whereas the K-means method obtains the highest grade in group 2. By contrast, the CLIQUE method shows the worst performance, with the lowest grades in groups 2, 3, and 4. Generally speaking, the SOM method consistencies most consistent with user judgment. Regarding the numerical performance, the existing indexes for evaluating clustering results are generally based on two criteria, i.e., Compactness and Separation [43]. Few indexes consider Uniformity, which measures the consistency of the sample size for each cluster. Uniformity is an important evaluation criterion because a large difference in the