## 4 Experimental Evaluation
The developed classifiers received lower results on the \"Tic-Tac\" dataset than the other algorithms (except DC). Figure 3 shows the comparison of the overall performance of the classifiers on average accuracy and other evaluation metrics such as \"Precision,\" \"Recall,\" and \"F-measure\" to see how reliable the achieved accuracies by associative classification models. Weighted (based on class distribution) average results of the Precision, Recall, and F-measure were reported, and the detailed information on evaluation metrics for each dataset is provided in Appendix A. The statistical differences were measured (wins/losses counts) on accuracy between CCC1, CCC2, CCC3, and other AC models (shown in Table 3). **W**: our approach was significantly better than the compared algorithm; **L**: selected rule-learning algorithm signif  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Dataset** & **MCC** & **CBA** & **SA** & **J&B** & **DC** & **DDC** & **CDC** & **CCC1** & **CCC2** & **CCC3** \\ \hline Breast.Can & 70.2 & 71.9 \(\pm\) 9.8 & 79.3 \(\pm\) 4.4 & 80.5 \(\pm\) 47 & 81.2 \(\pm\) 4.0 & 81.9 \(\pm\) 41 & **82.6 \(\pm\) 4.5**  
icantly outperformed our algorithm; N: no significant difference has been detected in the comparison. Table 3 shows that CCC1 obtained the comparable results with AC methods included in the comparison. However, CCC1 lost to J&B. CDC and CCC2 methods according to win/losses counts (CCC1 has more losses than wins compared to those algorithms); there was no statistical difference between them on the 8, 9, and 11 datasets out of 12. The same experiment was carried out for CCC2, as shown in Table 4.  
Interestingly, CCC2 did not get worse accuracy than DC and CCC3 methods on any datasets. Even though CCC2 lost to the CBA and CDC algorithms, it won the rest of the algorithms in accuracy due to wins/losses counts. Table 5 defines the wins/losses counts of the CCC3 method. The results showed that the CCC3 method obtained a slightly lower result than all other AC methods in terms of statistical win/loss counts except for DC and SA. However, on average, the classification accuracies of CCC3 are not much different from those of the other 8 associative classifiers. Experimental evaluations of the number of rules generated by classifiers are shown in Table 6 (The best result for each dataset was bolded). Since the size of DC, DDC (DC and DDC utilized the same \"direct\" distance metric for clustering, further producing the same number of rules), and CDC, CCC1 (CDC and CCC1 methods employed the same \"indirect\" distance metric for clustering) are the same, these algorithms are merged in the resulting table. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **CBA** & **SA** & **J\&B** & **DC** & **DDC** & **CDC** & **CCC3** \\ \hline
**W** & 2 & 5 & 3 & 2 & 2 & 0 & 2 \\ L & 3 & 2 & 2 & 0 & 1 & 3 & 0 \\ N & 7 & 5 & 7 & 10 & 9 & 9 & 10 \\ \hline \end{tabular}
\end{table} 
Table 4: Statistically significant wins/losses counts of CCC2 method on accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **CBA** & **SA** & **J\&B** & **DC** & **DDC** & **CDC** & **CCC2** & **CCC3** \\ \hline
**W** & 3 & 4 & 1 & 1 & 2 & 1 & 0 & 2 \\
**L** & 3 & 2 & 2 & 0 & 1 & 3 & 1 & 0 \\
**N** & 6 & 6 & 9 & 11 & 9 & 8 & 11 & 10 \\ \hline \end{tabular}
\end{table} 
Table 3: Statistically significant wins/losses counts of CCC1 method on accuracy. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **CBA** & **SA** & **J\&B** & **DC** & **DDC** & **CDC** & **CCC2** & **CCC3** \\ \hline
**W** & 3 & 4 & 1 & 1 & 2 & 1 & 0 & 2 \\
**L** & 3 & 2 & 2 & 0 & 1 & 3 & 1 & 0 \\
**N** & 6 & 6 & 9 & 11 & 9 & 8 & 11 & 10 \\ \hline \end{tabular}
\end{table} 
Table 3: Statistically significant wins/losses counts of CCC1 method on accuracy. Table 6 shows that the proposed methods achieved the second-highest result on the average number of rules, the only classifier that performed better was the DC classifier.