An Effective and Efficient Similarity-Matrix-Based Algorithm for Clustering Big Mobile Social Data

Abstract

Nowadays a great deal of attention is devoted to the issue of supporting big data analytics over big mobile social data. These data are generated by modern emerging social systems like Twitter, Facebook, Instagram, and so forth. Mining big mobile social data has been of great interest, as analyzing such data is critical for a wide spectrum of big data applications (e.g., smart cities). Among several proposals, clustering is a well-known solution for extracting interesting and actionable knowledge from massive amounts of big mobile (geo-located) social data. Inspired by this main thesis, this paper proposes an effective and efficient similarity-matrix-based algorithm for clustering big mobile social data, called TourMiner, which is specifically targeted to clustering trips extracted from tweets, in order to mine most popular tours. The main characteristic of TourMiner consists in applying clustering over a well-suited similarity matrix computed on top of trips. A comprehensive experimental assessment and analysis over Twitter data finally comfirms the benefits coming from our proposal.