학술논문

Clustering High-Dimensional Social Media Datasets utilizing Graph Mining
Document Type
Conference
Source
2022 IEEE International Conference on Big Data (Big Data) Big Data (Big Data), 2022 IEEE International Conference on. :3871-3880 Dec, 2022
Subject
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Geoscience
Robotics and Control Systems
Signal Processing and Analysis
Couplings
Social networking (online)
Blogs
Machine learning
Big Data
Data mining
Problem-solving
Social Network Analysis
Graph Mining
Twitter
Knowledge Extraction
Clustering
Dimensionality Reduction
Language
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.