# Efficient Rule Generation for Associative Classification
## 5 Experimental Setting and Result
The accomplishment of the proposed algorithm is the discovery of the most efficient CAR in each iteration and the elimination of unnecessary transaction IDs that leads to redundant CARs. Table 12 shows the average classifier building time of the proposed algorithm against CBA, CMAR, and FACA. The experimental result clearly shows that our algorithm is the fastest among all algorithms in the 14 datasets. ECARG takes fewer seconds to construct the classifier than CBA, CMAR, FACA, and ECARG2 by 2.134, 0.307, 2.883, and 0.0162, respectively. This can be explained by the fact that CBA and FACA uses an Apriori-style approach to generate candidates. When the value for minimum support is low on large datasets, it is costly to handle a large number of candidate ruleitems. The CMAR algorithm based on FP-growth is better than CBA and FACA in some cases, but it takes more classifier-generating time than ECARG and ECARG2. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Datasets** & **CBA** & **CMAR** & **FACA** & **ECARG** & **ECARG2** \\ \hline Anneal & 83.19 & 73.27 & 87.31 & 95.21 & **96.77** \\ Breast & 67.16 & **74.83** & 72.44 & 70.33 & 73.02 \\ Cars & 78.29 & 73.73 & 70.02 & 73.43 & **87.79** \\ Contact & 66.67 & 37.5 & 63.33 & **70.83** & 65.00 \\ Diabetes & **74.47** & 57.03 & 73.56 & 67.32 & 73.7 \\ Iris & 92.67 & **97.33** & 96.00 & 95.33 & 96.00 \\ Labor & 75.67 & 26.32 & 87.67 & **92.67** & 84.00 \\ Lymph & 77.76 & 43.24 & 82.43 & **88.51** & 81.81 \\ Mushroom & 93.40 & 86.25 & 96.52 & 98.15 & **98.9** \\ Post-oper. & 56.67 & **70.00** & 67.78 & **70.00** & 60.00 \\ Tic-tac-toe & **99.16** & 53.03 & 90.23 & 65.34 & 88.94 \\ Vote & 94.02 & 92.64 & 91.92 & **95.31** & 95.17 \\ Wine & 89.97 & 62.92 & 92.16 & **98.87** & 97.16 \\ Zoo & 60.27 & 79.21 & 86.00 & 95.00 & **96.00** \\ \hline Average & 79.24 & 66.24 & 82.67 & 84.02 & **84.42** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracies of CBA, CMAR, FACA, ECARG, and ECARG2. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Sets** & **CBA** & **CMAR** & **FACA** & **ECARG** & **ECARG2** \\ \hline Anneal & **3** & 165 & 15 & 14 & 17 \\ Breast & 16 & 127 & 23 & **3** & 35 \\ Cars & 25 & 272 & 9 & **5** & 18 \\ Contact & 9 & 25 & **5** & 7 & 8 \\ Diabetes & 56 & 115 & 24 & **4** & 38 \\ Iris & 11 & 38 & 7 & **4** & 9 \\ Labor & **8** & 297 & 15 & 9 & 9 \\ Lymph & 26 & 465 & **15** & 19 & 20 \\ Mushroom & **8** & 28 & 16 & 12 & 13 \\ Post-oper. & 35 & 51 & 12 & **11** & 27 \\ Tic-tac-toe & 28 & 713 & 12 & **6** & 33 \\ Vote & 30 & 658 & 12 & 11 & **10** \\ Wined & **5** & 237 & 11 & 9 & 7 \\ Zoo & **10** & 97 & 11 & **10** & **10** \\ \hline Average & 19 & 240 & 13 & **8** & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The average number of generated rules on the UCI datasets. Table 13 reveals the memory consumption in the classifier building process of all 5 algorithms. The results show that ECARG consumes less memory than CBA, CMAR, FACA, and ECARG2 by 22.62 MB, 73.15 MB, 36.57 MB, and 0.98 MB, respectively. The memory consumption of ECARG is the best since it eliminates unnecessary data in each iteration. From the result in Table 14, our proposed algorithm gives a higher F-measure on average than the other algorithms. In particular, the ECARG2 outperformed CBA, CMAR, FACA, and ECARG by 3.82%, 25.38%, 25.38%, 12.74%, and 1.84%, respectively.