Clustering High-Dimensional Social Media Datasets sutilizing Graph Mining

Andreas Kanavos, Gerasimos Vonitsanos, Phivos Mylonas

Abstract: Social networks are an essential component of people’ daily lives, and as a result, much academic attention has been focused on them. The rapid adoption of machine learning as a problem-solving tool, which simplifies and accelerates numerous tasks while enabling the processing of large volumes of data, has played a significant role in this field of research. This is in contrast to the more traditional approaches that lacked this momentum. Characterization of linkages and cluster identification i n social networks are two of the research community’s most well-known issues. The goal of this study is to gather data for a set of users who are then divided into groups based on the hashtags they used in their Twitter postings. The procedure performed generates the numerical data, in following reduces the dimensions, and finally performs the clustering.