Sanchari Chatterjee1, Angelina A. Tzacheva2, and Zbigniew Ras1, 1Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA, 2Computer Science and Information Technology, College of Computing and engineering, WestCliff University, Irvine, CA 92614
Education sector, Business field ,Medical domain and Social Media, huge amounts of data in a single day . Mining this data can provide a lot of meaningful insights on how to improve user experience in social media, users engage in these domains collect and cherish the data as they hope to find patterns and trends and the golden nuggets that help them to accomplish their goal. For example: How to improve student learning; how to increase business profitability; how to improve user experience in social media; and how to heal patients and assists hospital administrators. Action Rule Mining mines actionable patterns which are hidden in various datasets. Action Rules provide actionable suggestions on how to change the state of an object from an existing state to a desired state for the benefit of the user. There are two major frameworks in the literature of Action Rule mining namely Rule-Based method where the extraction of Action Rules is dependent on the pre-processing step of classification rule discovery and Object-Based method where it extracts the Action Rules directly from the database without the use of classification rules. Hybrid Action rule mining approach combines both these frameworks and generates complete set of Action Rules. The hybrid approach shows significant improvement in terms computational performance over the Rule-Based and Object-Based approach. In this work we propose a novel Modified Hybrid Action rule method with Partition Threshold Rho, which further improves the computational performance with large datasets.
Actionable Patterns, Action Rules, Emotion Detection, Data Mining, Rule-Based, ObjectBased.