Distributed classification for pocket data mining

F. Stahl, M. Gaber, Han Liu, Max Bramer, P. Yu

Research output: Contribution to conferencePaperpeer-review

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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 [1]. 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 Hoe�ding trees and Naive Bayes classifers 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 classifers.
Original languageEnglish
Publication statusPublished - 28 Jun 2011
EventProceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011) - Warsaw, Poland
Duration: 28 Jun 201130 Jun 2011

Conference

ConferenceProceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011)
Country/TerritoryPoland
CityWarsaw
Period28/06/1130/06/11

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