Paper Info
Title
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.
Year
DOI
Venue
2016
10.1109/ICMLA.2016.0091
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)
Keywords
Field
DocType
Big Data Analytics, Big Mobile Social Data, Big Data Clustering
Data science,Computer science,Artificial intelligence,Cluster analysis,Algorithm design,Algorithm,Social system,TRIPS architecture,Big data,Semantics,Mobile telephony,Machine learning,Similarity matrix
Conference
ISBN
Citations 
PageRank 
978-1-5090-6168-6
0
0.34
References 
Authors
19
4
Name
Order
Citations
PageRank
Gloria Bordogna1974103.99
Luca Frigerio200.68
Alfredo Cuzzocrea31751200.90
Giuseppe Psaila4722192.45