From concrete to inferred knowledge: Enhanced mining constraint-based cyclic association rules from medical social network

Abstract

In recent years, social network has been given much interest. The explosion of social network activity has lead to generation of massive volumes of user-related data and has given birth to a new area of data analysis. In parallel, the last decade witnessed a fastidious interest in the data mining which efficiently find hidden knowledge that can be extracted from applied information, namely, social data. Among the most used data mining techniques, we particularly focus on cyclic constraint-based association rules. In this paper, we aim to derive significant cyclic constraint-based association rules from social data. Thus, we introduce a new approach EMC 2 for social mining through constraint-based cyclic association rules extraction. The encouraging experimental results carried out prove the usefulness of our approach.