# High-Performance Actionable Knowledge Miner for Boosting Business Revenue
## 2 Methods
### A Distributed Algorithm for the Mining of Action Rules
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Type 0** & **Type 1** & **Bin-Type 1** \\ \hline A: Action rules extracted & 24,515 & 5153 & 14,233 \\ B: Action rules read into RS & 5686 & 1230 & 3066 \\ C: Ratio B/A & 23.2\% & 23.9\% & 21.5\% \\ D: Atomic actions & 137 & 54 & 44 \\ E: Atomic actions triggered & 135 & 54 & 44 \\ F: Triggered action rules & 5555 & 1230 & 3066 \\ G: Ratio: F/B & 97.7\% & 100\% & 100\% \\ H: Meta-actions extracted & 9 & 9 & 9 \\ I: Effective meta-nodes & 18 & 5 & 6 \\ J: Max NPS impact & 8.58\% & 7.37\% & 1.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of meta-action mining, triggering, and meta-node creation processes for a medium dataset with different modifications for the rule types. ## 4 Discussion

### Small Datasets

On small datasets, we were able to achieve significantly faster rule mining with changing the rule type; however, using categories of values did not bring such an effect. The coverage of customers was not impacted by the changes. For the small datasets, the mining usually completes within seconds, but these preliminary experiments provided insight into the magnitude of the potential performance improvement. ### Medium Datasets

Medium datasets created performance issues in their original size, and the proposed modifications brought significant improvements. In this case, categorizing benchmark values also resulted in shorter mining times. ### Distributed Algorithm

From the presented results, one can see that the distributed algorithm takes much less time to run. This improvement is important for the cases that originally run for hours, or even days, because it shortens it to minutes. In addition, the coverage was not worse than for the nondistributed algorithm. Figure 8: Recommendations generated for Client 32, Category Service, 2016, based on extracted rules of “Type 1”. Figure 7: Recommendations generated for Client 32, Category Service, 2016, based on extracted rules of “Type 0”. ### Impact on Recommendations

Figures 7 and 8 show that changing the algorithm to Rule--Type 1 resulted in fewer recommendations. Tests conducted for other companies with Rule--Type 1 also resulted in generating fewer meta-nodes and fewer meta-actions in a meta-node than with the original rule type. This results from the way a triggering procedure was implemented--Rule--Type 1--target a greater number of matched customers and there is less differentiation between these. Since the target value of flexible attributes in action patterns was changed to \"higher\", the resulting rules aggregate more customers with the new rule type. More customers are associated with a single action rule and, accordingly, more meta-actions. As a matter of fact, most action rules extracted are associated with all extracted meta-actions. As a result, the meta-nodes generating procedure tends to produce nodes that encompass all extracted meta-actions. As a side effect, the functionality of the recommender system deteriorates. Meta-nodes (recommendations) that involve all meta-actions require businesses to change all areas of their services. As Figure 8 shows, the recommendable items, depicted as bubbles, are either \"all\" meta-actions or only 1-2 meta-actions, but nothing \"in-between\". For smaller datasets, no, or one, recommendation is created. ## 5 Conclusions

Within this work, we proposed, implemented, and evaluated a number of methods to improve action rule mining performance. With regard to the modification of the NPS recommender system, eventually Rule--Type 1 was not incorporated due to resulting in much fewer recommendation options. This modification can, however, provide an alternative in case the mining processes are taking too long for larger datasets. The distributed implementation seems promising due to large performance improvements in the times of mining for the action rules. It is expected that this method will greatly improve the scalability of the system.