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.
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Stahl, F. et al. (2012). Homogeneous and Heterogeneous Distributed Classification for Pocket Data Mining. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems V. Lecture Notes in Computer Science, vol 7100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28148-8_8
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DOI: https://doi.org/10.1007/978-3-642-28148-8_8
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