Affiliations: [a] Computer Science Department, High Institute of Management, Tunis, Tunisie | [b] Computer Science Department, National School of Computer Sciences, Tunis, Tunisie
Correspondence:
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Corresponding author: Wafa Tebourski, Computer Science Department, High Institute of Management, Tunis, Tunisie. E-mail:[email protected]
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 EMC2 for social mining through constraint-based cyclic association rules
extraction. The encouraging experimental results carried out prove the
usefulness of our approach.
Keywords: Social network, Twitter, medicine, data mining, data warehouse, constraint, cyclic association rule, multidimensional association rules
