# Efficient Rule Generation for Associative Classification
## 2 Related Work
In addition, CARs are priors sorted by using the harmonic mean, which is an average value between support and confidence. The WCBA algorithm is more significantly accurate than CBA, CMAR, MCAR, FACA, and ECBA. However, the WCBA algorithm generates CARs based on the Apriori technique that scans the database many times. [PERSON] proposed [13] the Active Pruning Rule (APR) algorithm. The new pruning process was introduced in APR. CARs are ranked by confidence, support, and rule length. Each training instance is matched over a set of CARs. The first rule that matches an instance is added to the classifier. Then, instances containing the first rule are removed. The support and confidence of remaining rules are recalculated, and all CARs are re-ranked. The APR algorithm was proven to reduce the size of the classifier and to maintain predictive accuracy performance. However, the APR algorithm still has to face a massive number of candidates from a rule-generation process. From previous works, the advantages and disadvantages are shown in Table 1.  
The previous algorithms on AC generally result in high predictability of rules. However, most of them produce k-ruleitems from (k \(-\) 1)-ruleitems. They have to calculate supports when a new ruleitems is recovered. To calculate support and confidence values, they have to search all transactions in databases multiple times. Moreover, a huge number of candidate CARs are generated and pruned later to reduce unnecessary CARs. To reduce the problems, the proposed algorithm will directly generate efficient CARs for classification so that the pruning and sorting processes are not necessary. The efficient CARs in our works are rules with 100% confidence which are generated based on the idea that some attribute values can immediately indicate the class label if all attribute values belong to a class label. To easily check attribute values belonging to any class label, vertical data representation is used in the proposed algorithm. Furthermore, simple set theories, intersection, and set difference are adapted to easily calculate support and confidence values without scanning a database multiple times. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Algorithms** & **Advantage** & **Disadvantage** \\ \hline \multirow{3}{*}{CBA} & It adopted the association rule technique to classify data that is proven to be more accurate than the traditional classification technique. & It has to face a sensitivity of the minimum support threshold. A massive number of rules are generated when a low minimum support threshold is given. \\ \hline \multirow{2}{*}{CMAR} & It uses an efficient FP-tree, which consumes less memory and space & The FP-tree will not always fit in the main memory, especially when the number of attributes is large. \\  & compared to CBA. & \\ \hline \multirow{2}{*}{eMCAC} & It adopts vertical data representation to reduce space usage to find a multi-label class. & It is based on an Apriori-like technique that can result in a large number of frequent itemsets. \\ \hline \multirow{2}{*}{FACA} & Set difference is adopted to consume low memory and to reduce the mining time. & It is based on an Apriori-like technique; therefore, the algorithm is required to search for all frequent itemsets from all possible candidate itemsets at each level. \\ \hline \multirow{2}{*}{PCAR} & It uses predictability value to prune unnecessary rules. & The execution time is slow since it includes the inner cross-validation phase for calculation predictability value. \\ \hline \multirow{2}{*}{WCBA} & It uses a weighted method to select useful rules and to improve the performance of the classifier. & Weighted factors are subject to change due to the decisions of experts which can cause a different experimental result. \\ \hline \multirow{2}{*}{APR} & A new evaluation method with a small classifier and high accuracy rate. & Generation of a large number of rules when a low minimum support threshold is given. \\ \hline \hline \end{tabular}
\end{table}