# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 1 Introduction
Each record in the dataset represents a survey with features describing the company, the particular service assessed, and the customer surveyed. Feedback is represented by numerical scores in different areas and by additional notes in the free-form text. The Net Promoter Score (NPS) is used to partition the customers into three different groups: promoters, passive, and detractors. The goal is to extract action rules showing what minimal business changes are needed in the repair shops in order to change the customers' status from passive to promoter, from detractor to passive, and from detractor to promoter. As already mentioned, action rules are composed of so-called stable attributes, flexible attributes, and target attributes. For stable attributes, the characteristics of a survey type and characteristics of a customer were chosen for the experimental setup. For flexible attributes, we chose benchmark questions from surveys, which represented areas of improvement in the customer satisfaction problem. The target attribute had three values: promoter, passive, and detractor. In other work, the authors present a Basic Score Classification Database (BSCD), which describes associations between different scales, regions, genres, and jumps [7; 12]. This database is used by a knowledge-based system to automatically index all submitted or retrieved pieces of music by emotion. Action rules extracted from the BSCD are used by a knowledge-based recommendation system to create solutions (automatically generated hints) that permit developers to manipulate a composition by retaining the music score while simultaneously varying the emotions it invokes. By score, we mean a written form of a musical composition. [PERSON] and [PERSON] [13] discuss a CO2 sensor network deployed in 16 equally divided parts of a classroom at UNC-Charlotte. Each part is equipped with one sensor node for CO2 concentration monitoring. Higher occupancy in the room triggers a higher value of CO2 concentration. Sensors in close proximity to people have higher CO2 readings. Personalized knowledge-based recommendation systems can be built to monitor indoor air quality inside the classroom and give recommendations on different strategies that can be followed to improve its air quality. All of the above examples of knowledge-based recommendation systems show a variety of application domains where action rules can be successfully used. However, in many cases, action rules need to be discovered from large or very large datasets, not only those containing a large number of tuples but also a large number of attributes. Mining for action rules efficiently in such datasets can be a challenge. As the volume and complexity of data continue to grow, there is a critical need for methods that can efficiently discover action rules from large datasets. To target this problem, we propose a new strategy for generating action rules based on the vertical partitioning of a dataset driven by correlations between its attributes. The idea is quite simple. We know, from rough sets theory that the more correlated attributes are in a dataset, the smaller its reducts are. Similarly, action rules should be shorter when discovered from datasets having correlated attributes. Therefore, instead of discovering action rules from a given dataset, we partition its attributes into clusters by treating the correlation as the distance measure. The more correlated the attributes are, the more close they are to each other in terms of distance. Our plan is to conduct hierarchical clustering and determine which level in the hierarchical tree provides the most effective attribute partition. Meaning, we iterate between each level to determine the optimal set of discovered action rules, defined by measures such as the number of rules, confidence, coverage, and lightness. In this paper, we discuss our proposed method of distributed action rule discovery based on attributes correlation and vertical data partitioning and report on its performance as compared to other methods.