Homogeneous and heterogeneous distributed classification for pocket data mining (original) (raw)

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.