Paper 8

Homogeneous and Heterogeneous Distributed Classification for Pocket Data Mining

Authors: Frederic Stahl, Mohamed Medhat Gaber, Paul Aldridge, David May, Han Liu, Max Bramer, and Philip S. Yu

Volume 5 (2012)

Abstract

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Ad- vances 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 classi cation techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/di erent, or ho- mogeneous/similar data stream classi cation techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.