Study of Data Mining Algorithms on Social Network Data for Discovering Invisible Patterns of Social Collaboration

The increased usage of social media by Internet users is generating user-generated eco-logical data, such as text and photos. Popular social networking sites such as Google+, Twitter, and Facebook get a disproportionately large level of Internet traffic. They have a plethora of data about their customers and the links that bind them. There are required to explore and store valuable data from the massive social network datasets, graph-based mining tools, which can simply recreate the structure of the social networks. There are several data analysis tools accessible, each with its own set of benefits and features. Clustering, classification, association, and regression are some of the methods that are utilized to extract useful information from large amounts of data. This technology has a variety of applications in the real world. This paper summarizes data mining technologies and algorithms. The Nystrom technique is the most popular data mining technique to identify the hidden patterns of social collaboration.