MR - Random Forest Algorithm for Distributed Action Rules Discovery

Action rules, which are the modified versions of classification rules, are one of the modern approaches for discovering knowledge in databases. Action rules allow us to discover actionable knowledge from large datasets. Classification rules are tailored to predict the object’s class. Whereas action rules extracted from an information system produce knowledge in the form of suggestions of how an object can change from one class to another more desirable class. Over the years, computer storage has become larger and also the internet has become faster. Hence the digital data is widely spread around the world and even it is growing in size such a way that it requires more time and space to collect and analyze them than a single computer can handle. To produce action rules from a distributed massive data requires a distributed action rules processing algorithm which can process the datasets in all systems in one or more clusters simultaneously and combine them efficiently to induce single set of action rules. There has been little research on action rules discovery in the distributed environment, which presents a challenge. In this paper, we propose a new algorithm called MR – Random Forest Algorithm to extract the action rules in a distributed processing environment.