Technological advances in the current era of big data have made it easy for the high-speed collection and generation of large volumes of a wide variety of valuable data (which may be of different veracity including precise, imprecise and uncertain data). As rich sources of big data, social networks consist of social entities who are often linked by some interdependency such as `following' relationships. Since these big social networks have been growing, there are real-life situations in which an individual user wants to find those frequently followed groups of social entities so that he can follow the same groups. Discovery of these frequently followed groups can be challenging because the social networks are usually big with lots of social entities. In this paper, we present a social data compression scheme and its associated big social data mining algorithm for the discovery of `following' relationships. Evaluation results show the practicality of our compression scheme and its associated algorithm for mining big social data in a cloud computing environment.
Data Mining algorithms
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask,hadoop Frontend :-python Backend:- MYSQL
₹10000 (INR)
2019