# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 6 Discussion and Conclusions  
This paper addresses action rule extraction from large datasets, a task that can be approached using two methods: rule-based and object-based. In the rule-based approach, the first step involves discovering classification rules, followed by examining their pairs to check if action rules can be constructed. There are known software packages available for generating these rules, such as Lisp-Miner [24] and SCARI [37]. However, a limitation of current action rule discovery packages, including Lisp-Miner and SCARI, is that they generate only small subsets of all classification rules, resulting in a much smaller set of action rules. Consequently, achieving sufficient coverage for the resulting action rules classifiers becomes problematic. Moreover, when the number of discovered action rules is limited, their lightness decreases. By lightness, we refer to the average number of action rules that support objects in a dataset. For instance, if the lightness is two, only two action rules can be applied to reclassify objects. It can happen that both of these options are either too costly or,for various reasons, unacceptable to users. Therefore, in such cases, they can not be applied. In the domain we tested, business owners expected to have a lightness measure of about ten. If the lightness measure exceeds this value, they prefer to see the top ten best action rules (using the visibility measure [36]). In the object-based approach, action rules are discovered directly from a dataset, resulting in a larger number of rules than following a rule-based approach. However, we are unaware of any software package discovering almost all action rules. One possibility is system ARED, which generates action schemas covering all action rules (see [41]). The question arises as to how to generate all or almost all action rules from these action schemas. Many datasets are large or very large. One potential strategy for extracting action rules from them is to begin by splitting the dataset vertically and/or horizontally, extracting action rules from these splits, and subsequently merging them. In this paper, we focused on knowledge discovery methods based on splitting dataset schemas into vertical clusters, comparing our proposed correlation vertical partitioning to both random vertical partitioning and the base (no partitioning) method. Our comprehensive evaluations on subsets of the NPS dataset highlight crucial differences between these methodologies. Specifically, the results in Table 2 demonstrate that the random partition generally yields fewer action rules compared to methods based on attribute correlation. Furthermore, when examining metrics such as precision and lightness across the datasets, methods grounded in attribute correlations consistently outperformed those based on random vertical partitioning, producing results comparable to the base method. For instance, the precision achieved using the correlation-driven approach exhibited either a notably higher or comparable precision compared to its random counterpart. This trend was similarly observed in terms of lightness across all datasets, indicating the broader applicability of the correlation-based technique. High lightness, as observed in our results, indicates the presence of a more extensive set of action rules, and therefore options for a user to explore. From a business standpoint, this is invaluable. Business stakeholders often require diverse options to weigh against varying costs, safety concerns, and potential outcomes of action rule suggestions. The fact that correlation-based partitioning offers this versatility, coupled with its consistent performance and reduced need for iterative runs, establishes it as a preferred method, particularly in scenarios with extensive datasets or where the quality and diversity of discovered action rules are crucial. In conclusion, our research highlights the advantages of leveraging attribute correlations in vertical partitioning for action rule discovery.