# Efficient Rule Generation for Associative Classification
## 6 Conclusions  
This paper proposes algorithms to enhanced associative classification. Unlike the traditional algorithms, the proposed algorithms do not need a sorting and pruning process. Candidate generation is carried out by attempting to select a first general rule with the highest accuracy. Moreover, a search space is reduced early by cutting down items with low statistical significance. Furthermore, a vertical  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Sets**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**\# of Rules**} & \multicolumn{2}{c}{**Building Time**} & \multicolumn{2}{c}{**Memory**} & \multicolumn{2}{c}{**F-1**} \\  & **AVG** & **S.D.** & **AVG** & **S.D.** & **AVG** & **S.D.** & **AVG** & **S.D.** & **AVG** & **S.D.** \\ \hline Anneal & 95.21 & 2.10 & 14 & 0.94 & 0.123 & 0.05 & 10.68 & 0.03 & 61.24 & 6.98 \\ Breast & 70.33 & 5.10 & 3 & 1.40 & 0.007 & 0.01 & 1.92 & 0.02 & 58.42 & 1.78 \\ Car & 73.43 & 6.11 & 5 & 0.82 & 0.057 & 0.05 & 3.05 & 0.00 & 45.81 & 7.16 \\ Contact & 70.83 & 28.81 & 7 & 0.92 & 0.001 & 0.00 & 1.78 & 0.02 & 71.67 & 30.54 \\ Diabetes & 67.32 & 6.75 & 4 & 1.34 & 0.032 & 0.01 & 3.01 & 0.03 & 56.56 & 3.41 \\ Iris & 95.33 & 5.44 & 4 & 0.52 & 0.004 & 0.00 & 1.17 & 0.00 & 90.41 & 5.48 \\ Labor & 92.67 & 14.05 & 9 & 1.26 & 0.005 & 0.00 & 1.95 & 0.02 & 88.89 & 13.72 \\ Lymph & 88.51 & 10.00 & 19 & 3.37 & 0.016 & 0.00 & 2.86 & 0.00 & 81.94 & 15.5 \\ Mushroom & 98.15 & 0.32 & 12 & 0.00 & 4.049 & 0.64 & 24.27 & 0.03 & 96.58 & 0.31 \\ Post-oper & 70.00 & 17.41 & 11 & 3.34 & 0.008 & 0.00 & 2.03 & 0.02 & 54.45 & 14.21 \\ Tic-tac-toe & 65.34 & 6.16 & 6 & 2.13 & 0.101 & 0.06 & 4.73 & 0.06 & 95.44 & 3.85 \\ Vote & 95.31 & 2.72 & 11 & 2.26 & 0.034 & 0.01 & 3.09 & 0.03 & 93.82 & 2.84 \\ Wined & 98.87 & 2.34 & 9 & 0.53 & 0.007 & 0.00 & 1.82 & 0.03 & 98.65 & 2.31 \\ Zoo & 95.00 & 6.99 & 10 & 0.70 & 0.020 & 0.01 & 2.20 & 0.03 & 91.76 & 13.65 \\ \hline Average & 84.02 & 8.16 & 8 & 1.395 & 0.320 & 0.06 & 4.61 & 0.02 & 78.25 & 8.70 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Standard deviations of ECARG.  
data format, intersection, and set difference methods are applied to calculate support and confidence and to remove unnecessary transaction IDs, decreasing computation time and memory consumption.  
The experiments were conducted on 14 UCI datasets. The experimental results show that the ECARG algorithm outperforms the CBA, CMAR, and FACA algorithms in terms of accuracy by 4.78%, 17.79%, and 1.35%, respectively. Furthermore, ECARG generates smaller rules than the other algorithms in almost all datasets. In addition, ECARG results in the most optimal classifier-generating time and memory usage on average. We can conclude that the proposed algorithm gives a compact classifier with a high accuracy rate, improves computation time, and reduces memory usage.  
However, the ECARG algorithm does not well perform on imbalanced datasets, such as Breast, Car, Diabetes, and Post-operative. This is because the ECARG algorithm tends to find 100% confidence CARs and to eliminate unnecessary transactions. Therefore, ruleitems belonging to minority classes will not meet the minimum support threshold or 100% confidence and they are eliminated accordingly. Consequently, the classifier cannot classify the minority class correctly.  
Methodology, C.T.; supervision, P.S. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by Mahasarakham University (Grant year 2021).  
The authors declare no conflict of interest.