Accessibility navigation


Homogeneous and heterogeneous distributed classification for pocket data mining

Stahl, F., Gaber, M. M., Aldridge, P., May, D., Liu, H., Bramer, M. and Yu, P. S. (2012) Homogeneous and heterogeneous distributed classification for pocket data mining. In: Hameurlain, A., Küng, J. and Wagner, R. (eds.) Transactions on large-scale data and knowledge-centered systems V. Lecture Notes in Computer Science (7100). Springer, pp. 183-205. ISBN 9783642281471

[img]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.

1MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Abstract/Summary

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.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:30161
Publisher:Springer

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation