Analysing social media with an Improved K-Means Clustering Algorithm

Nowadays, sharing human social behavior and growing a multi-user network requires the use of social media. Analysis of social media provides a valuable opportunity to study human social activity at scale. People often bring and talk about different kind of topics on social media platforms. depending on which, discussions are made to show the positive as well dis positives ways. This is an interesting point about using social media. There is a great deal of information about the type and substance of these discussion holography’s that informs us more broadly around patterns in social media interactions, and how content flows between individuals. Data mining techniques exists which can be utilized to crunch out user information from social media and relationships existing within the network. However, state of the art methods often fail to model user communities and their behaviors at a correct level. To solve this problem and help successfully do the grouping of related information, in our work here we present an enhanced fuzzy means clustering approach. Using this technique reduces the creation and time complexity of clusters, resulting in higher quality group outcomes. Our proposed system aims at mitigating the problems as described above by introducing a scalable and effective solution for real-time cluster social media data.