## 3 Results and Discussion
### Experiments on Mixed Datasets  
In this section, we investigated the performance of the iterative clustering algorithm based on the proposed similarity measurement on mixed datasets. Table 1 shows the information of each dataset. Note that the second column presents the number of samples, the third column presents the number of the two types of attributes, and the last column presents the probability distribution of samples in different classes. To evaluate the performance of the iterative clustering algorithm based on the proposed similarity measurement, we compare its clustering results with OCIL and K-Prototype. The average value and standard deviation of the clustering error of these clustering algorithms are statistically summarized in Table 2. In the experiments, the weight parameter \(\gamma\) was set to 1.5 for the K-Prototypes algorithm. From Table 2, it can be observed that the iterative clustering algorithm based on the proposed similarity measurement outperforms the OCIL and K-Prototype methods for six datasets, although the ratios of the numbers of categorical attributes to numerical attributes differ greatly, as shown in Table 1. Compared to the other two methods, the iterative clustering algorithm can improve the accuracies of clustering results by 2.13% and 4.28%, respectively. Especially in the Heart dataset, the iterative clustering algorithm improves the accuracy by 2.85% and 5.86%, respectively. This result indicates that the proposed similarity measurement is applicable to mixed datasets of variant compound styles and does not need any parameter to give weights to the two types of attributes. Furthermore, for datasets that have very uneven class distributions, the proposed similarity measurement can also achieve adequate clustering results. To study why the iterative clustering algorithm based on the proposed similarity measurement outperforms the OCIL and K-Prototype methods, we analyzed the correlation between each attribute and the label attribute in the dataset by calculating the correlation coefficients in statistics. Since the label attribute is a categorical attribute, the Pearson correlation coefficient and the Spearman correlation coefficients are not suitable. The Kendall correlation coefficient requires that the categorical attribute be ordered; therefore, it also cannot be used to calculate the correlation between the categorical attribute and the label attribute. Here, we adopt the ReliefF algorithm, which can estimate the quality of dependencies between each attribute and label attribute [28]. To see whether the correlation between the dependencies and weights affects the clustering results, we calculate the Pearson correlation coefficient between the dependencies calculated by the ReliefF algorithm and the weights calculated by Equations (6) and (8) in each dataset. The result is shown in Table 3. Since the Dermatology dataset as well as the \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Instance** & **Attribute (\(d_{c}+d_{u}\))** & **Class** & **Class Distribution** \\ \hline Statlog(Heart) & 270 & \(7+6\) & 2 & 55.56\%, 44.44\% \\ Hepatitis & 155 & \(13+6\) & 2 & 20.65\%, 79.35\% \\ Cylinder Bands & 540 & \(19+20\) & 2 & 42.33\%, 57.67\% \\ Australian & 690 & \(8+6\) & 2 & 55.51\%, 44.49\% \\ Dermatology & 366 & \(33+1\) & 6 & 30.60\%, 16.67\%, 19.67\%, 13.39\%, 14.21\%, 5.46\% \\ Zoo & 101 & \(16+1\) & 7 & 40.59\%, 19.80\%, 4.95\%, 12.87\%, 3.96\%, 7.92\%, 9.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of mixed datasets.