Distributed classification for pocket data mining

Authors:
Frederic Stahl;Mohamed Medhat Gaber;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;Department of Computer Science, University of Illinois at Chicago, Chicago, IL
Venue:
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Year:
2011

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Abstract

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifiers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifiers.