An empirical research and comparative analysis of clustering performance for processing categorical and numerical data extracts from social media

Abstract

Social media has significantly influenced modern lifestyle and the way in which most of the industries operate their business. Social media data refers to the contents created by users during their social interactions in the form of text, sound, visuals, etc. It has now evolved as the major source of information for different industry verticals like retail, marketing, advertising, tourism, hospitality, education, etc. The huge volume of data resulted in the necessity for better and efficient procedures for personalized information retrieval. Traditional data mining and information retrieval techniques based on content-based and/or collaborative filtering proved computationally costly and less scalable against the volume it must deal with. Adoption of clustering techniques is a potential solution for this problem as it can minimize the amount of data required to be managed in industrial applications like recommender systems. This empirical research focuses on evaluating multiple clustering algorithms with the goal of finding an ideal solution for clustering numerical data extracted from social media sources. Three different publicly available datasets with varying number of attributes and records from tourism domain are used for the experiments conducted as part of this work