Abstract Purpose: The extraction of actionable insights is critical for intelligent systems and recommendation engines. However, traditional methods for action rule discovery face challenges in scalability and efficiency when applied to large datasets. This study introduces a correlation-based vertical partitioning method to improve the consistency and interpretability of action rules while addressing the limitations of random partitioning and unstructured approaches. Methods: The proposed method clusters flexible attributes using correlations, enabling structured partitions for parallel rule generation via hierarchical clustering. Comparative experiments evaluated its precision, runtime, lightness, and coverage against random and baseline partitioning approaches. Results: The correlation-based method outperformed random partitioning and significantly improved runtime efficiency over the baseline. It generates interpretable rules in a single iteration, avoiding variability and repeated runs, though challenges in rule combination efficiency suggest areas for improvement. Conclusion: The correlation-based vertical partitioning method strikes a balance between computational efficiency and rule quality, making it a promising solution for large-scale action rule discovery. Future work could enhance scalability further by improving the rule combination process and exploring hybrid or adaptive partitioning strategies to extend the method’s applicability across diverse domains.

Actionability is a mode of revealing actionable knowledge in the form of Action Rules from large datasets. Action rule imparts in the form of recommendations as how a data object can change from one value to another more desirable value. The towering production of data in the recent years, due to increased usage of web, social media and IoT, has led to the age of big data. Also, abundant usage of cloud storages and cloud based services causes the data to be spread around the globe. This requires more time and space for a single computer to cope with such widespread data. Ecosystems like Hadoop MapReduce, Spark have been introduced to store, process and retrieve back the data efficiently in a distributed fashion. Data mining finds substantial improvements over such distributed frameworks to process huge volume of data and acquire knowledge from them in a short span of time. In this paper, we present an approach SARGS: Specific Action Rule discovery based on Grabbing Strategy, to build more specific Action Rules using Apache Spark framework and evaluate the results with our previous Hadoop MapReduce system (MR-Random Forest Algorithm for Distributed Action Rules Discovery). Also, we propose a novel approach to distribute data in a distributed environment to get more optimal Action Rules and upgraded ARoGS algorithm to get more specific Action Rules.

Action rules (or actionable patterns) describe possible transitions of objects from one state to another with respect to a distinguished attribute. Strategies for discovering them can be divided into two types: rule based and object based. Rule-based actionable patterns are built on the foundations of preexisting rules. This approach consists of two main steps: (1) a standard learning method is used to detect interesting patterns in the form of classification rules, association rules, or clusters; (2) the second step is to use an automatic or semiautomatic strategy to inspect such results and derive possible action strategies. These strategies provide an insight of how values of some attributes need to be changed so the desirable objects can be shifted to a desirable group. Object-based approach assumes that actionable patterns are extracted directly from a database. System DEAR, presented in this paper, is an example of a rule-based approach. System ARD and system for association rules mining are examples of an object-based approach. Music Information Retrieval (MIR) is taken as an application domain. We show how to manipulate the music score using action rules. © 2011 Wiley Periodicals, Inc.

It is a great pleasure to edit this special issue dedicated to my friend Professor Jan M. Z ˙ytkow

Action rules assume that attributes in a database are divided into two groups: stable and flexible. In general, an action rule can be constructed from two rules extracted earlier from the same database. Furthermore, we assume that these two rules describe two different decision classes and our goal is to reclassify objects from one of these classes into the other one. Flexible attributes are essential in achieving that goal because they provide a tool for making hints to a user about what changes within some values of flexible attributes are needed for a given group of objects to reclassify them into a new decision class. A new subclass of attributes called semi-stable attributes is introduced. Semi-stable attributes are typically a function of time and undergo deterministic changes (e.g., attribute age or height). So, the set of conditional attributes is partitioned into stable, semi-stable, and flexible. Depending on the semantics of attributes, some semi-stable attributes can be treated as flexible and the same new action rules can be constructed. These new action rules are usually built to replace some existing action rules whose confidence is too low to be of any interest to a user. The confidence of new action rules is always higher than the confidence of rules they replace. Additionally, the notion of the cost and feasibility of an action rule is introduced in this article. A heuristic strategy for constructing feasible action rules that have high confidence and possibly the lowest cost is proposed. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 719–736, 2005.

In this article we show the role of equations and rules as definitions of attribute values. Such definitions can be used in many ways but in this article we concentrate on their applications to intelligent query answering. They are used as a tool for knowledge exchange between independently built knowledge systems.The Intelligent Query Answering System (IQAS) decides which sites are optimal for extracting definitions needed to solve a query. These optimal sets are identified by a search strategy based on rough sets theory, introduced by Z. Pawlak.8 We introduce the notion of shared operational semantics. To put the shared operational semantics on a firm theoretical foundation we proposed a formal interpretation that justifies empirical equations and rules in their definitional role. © 2002 John Wiley & Sons, Inc.

In this paper we are interested in necessary and sufficient conditions needed to learn definitions of non-local attributes in DAKS (see [15], [1.4]) assuming that the confidence of what we learn is kept possibly the highest. The notion of a reduci introduced in Rough Sets theory seems to be quite useful in solving this problem. For every non-local attribute a and local attributes A, we introduce the notion of < a, A >-directed set of reducts which basically models the strategy of search for a definition of a in DAKS.