# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 1 Introduction  
Every day, massive amounts of data are generated from a multitude of sources, from social media to scientific research. Analyzing and extracting useful insights from these vast datasets can be a daunting task, but action rules provide a powerful tool for identifying patterns and relationships within the data. They can help uncover hidden connections that may be invisible to human analysts. However, generating action rules efficiently for datasets with a large number of attributes remains a significant challenge. Let us assume that objects in a dataset are described by a group of classification attributes and a single distinguished attribute called the decision attribute. Classification attributes can be further divided into stable and flexible. The values of flexible attributes, describing objects, can change in time, whereas values of stable attributes stay the same. Stable attributes are mainly used for the personalization of data mining results. Action rule discovery is a type of data mining method used to reclassify objects with respect to a decision attribute. The idea here is to identify sets of minimal changes in flexible attributes, describing certain objects, that would lead to a desired change in the decision value for these objects. Action rules have been used to build a subclass of recommendation systems called knowledge-based recommendation systems. Domains of application include, among others,business (see [1; 2; 3]), healthcare (see [4; 5; 6]), music (see [7]), and art (see [8; 9]). Next, we give a brief overview targeting some of these application areas. The authors of [5] present a strategy to build a procedure graph for each medical procedure that takes place in a hospital. The procedure graph models sequences of consecutive procedures that follow the same initial procedure undertaken with a group of patients in a hospital. Data publicly available in the Florida State Inpatient Database (SID), which is a part of the Healthcare Cost and Utilization Project (HCUP) [10], were used for that purpose. The paper also presents a collection of hierarchically structured recommendation systems used for decreasing the number of re-admissions to hospitals based on action rules extracted from the SID. To build these recommendation systems, patients in the SID were hierarchically partitioned into subgroups using their common characteristics as the filtering tool. This process is called the personalization of patients; it increases the predictability of the following procedures represented as nodes in the procedure graphs and also decreases the anticipated number of re-admissions. Here, action rules represent medical recommendations that can be used by physicians for placing a patient on the shorter, more successful, and safer procedure path. Another work [11] presents a recommendation system for the surgical treatment of [PERSON]'s disease. This kind of surgery is called deep brain stimulation (DBS). During DBS surgery, a set of three to five parallel-reading micro-electrodes are inserted into the patient's brain. The electrodes are directed toward the expected location of the target nucleus, which is a small structure placed deep in the brain called the STN that does not show well in CT or MRI scans. At each desired depth, the reading electrodes record the electrophysiological activity of surrounding brain tissue. These electrodes advance until at least one of them passes through the nucleus, but it still requires an experienced neurologist/neurosurgeon to tell whether the recorded signal comes from the STN or not. The recommendation system presented in [11] answered this question with a confidence close to 100 percent. After such an electrode is verified, it is replaced by a permanent stimulating electrode. [PERSON] and [PERSON] [2] present a recommendation system based on knowledge (action rules) extracted from a sample dataset of customer feedback (around 300,000 records) concerning repair shops for caterpillar equipment covering 34 companies located in the US and Canada.