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The contemporary age of big data is characterized by rapid improvements in information and
communications technology, which have facilitated the rapid creation and collecting of massive
amounts of diverse and useful data (which may be of different veracity including precise,
imprecise and uncertain data). Social networks, which are rich sources of big data, are made up
of social entities that are often connected with one another by interdependency, such as
‘following’ connections. Because these large social networks have been expanding, there are
now instances in real life in which an individual user want to discover those groups of social
entities that are commonly followed by other users so that he might follow the same groups.
The discovery of these regularly followed groups might be difficult because social networks are
often extremely large and include a great number of social entities. In this research, we
introduce a social data compression technique and its accompanying massive social data mining
algorithm for the purpose of locating associations that may be described as “following.” The
results of the evaluation demonstrate that our compression method and the algorithm that is
linked with it are realistic for mining large amounts of social data in a cloud computing

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