Providing an Ontology-Based Method for Exploring the Association Rules in Multi-Agent Distributed Environments

Distributed association rules mining is one of the most important data mining methods that extracts the inter dependence of data items from decentralized data sources, regardless of their physical location and is based on the process of extracting repeated items. When exploration algorithms are implemented on   large-scale data, a large number of recurring items are produced, many of which are irrelevant,            ambiguous, and unusable for the business, thus causing a challenge called "combination explosion ". In this paper, a new coalition method based on distributed data mining and domain archeology, abbreviated to DARMASO, is proposed to address this challenge. This method uses three algorithms: the   DARMASOMAIN algorithm to guide and control the process of exploration and aggregation of universal rules, the DARMASOPRU algorithm to reduce and prune the data and the DARMASOINT algorithm to explore and aggregate the rules of all the generated data sources. DARMASO uses a map-reduce-based distributed computational model in a multi-agent distributed environment. It also provides a practical way for semantic mining of large-scale data sets. This method filters out the association rules of generality based on the purposes of data mining as well as the needs of the user and only produces and maintains useful rules. Reducing the scope of exploration and filtration of rules is achieved through the process of semantic pruning in the form of removing inappropriate candidates from the set of frequent items and    producing association rules of utility. The implementation is performed using a data set from the scope of natural disasters and the earthquake class. It also improves the speed and quality of rule extraction and generates practical, reliable, logical, quality and valuable rules to support decision-making amid the  masses of data.