Spreading activation model for connectivity based clustering

Authors:
QingYuan Huang;JinShu Su;YingZhi Zeng;YongJun Wang
Affiliations:
School of Computer, National University of Defense Technology, ChangSha, Hunan, China;School of Computer, National University of Defense Technology, ChangSha, Hunan, China;School of Computer, National University of Defense Technology, ChangSha, Hunan, China;School of Computer, National University of Defense Technology, ChangSha, Hunan, China
Venue:
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Year:
2006

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Abstract

Connectivity based clustering has wide application in many networks like ad hoc networks, sensor networks and so on. But traditional research on this aspect is mainly based on graph theory, which needs global knowledge of the whole network. In this paper, we propose a intelligent approach called spreading activation models for connectivity based clustering (SAMCC) scheme that only local information is needed for clustering. The main feature of SAMCC scheme is applying the idea of spreading activation, which is an organization method for human long-term memory, to clustering and the whole network can be clustered in a decentralized automatic and parallel manner. The SAMCC scheme can be scaled to different networks and different level clustering. Experiment evaluations show the efficiency of our SAMCC scheme in clustering accuracy.