Homogeneous and heterogeneous distributed classification for pocket data mining

Authors:
Frederic Stahl;Mohamed Medhat Gaber;Paul Aldridge;David May;Han Liu;Max Bramer;Philip S. Yu
Affiliations:
School of Computing, University of Portsmouth, Portsmouth, UK;School of Computing, University of Portsmouth, Portsmouth, UK;School of Computing, University of Portsmouth, Portsmouth, UK;School of Computing, University of Portsmouth, Portsmouth, UK;School of Computing, University of Portsmouth, Portsmouth, UK;School of Computing, University of Portsmouth, Portsmouth, UK;Department of Computer Science, University of Illinois at Chicago, Chicago, IL
Venue:
Transactions on Large-Scale Data- and Knowledge-Centered Systems V
Year:
2012

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Abstract

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.