Speaker: Ramandeep Kaur
Day:  Wednesday, 3/01/2006
Room: ITEB 336
Time: 3:30pm
Title: SACA: SCM-based Adaptive Clustering Algorithm

Network clustering is an important technique widely used in efficient
hierarchical routing protocol design, network modeling and performance
evaluation, etc. This paper, discuss the important clustering criteria,
such as node connectivity, cluster diameter, number of orphan nodes. Its
main contribution is a novel clustering algorithm SACA based on an accurate
clustering measure called SCM. SACA adaptively forms clusters to
incrementally improve the clustering quality, taking node connectivity into
consideration. It can control the cluster size effectively and limit the
number of orphan nodes. Its simulation study indicates that SACA is more
accurate than MCL, a well accepted scalable and efficient clustering
scheme, while requiring comparable running time for power law topologies
and grid topologies, and significantly less running time for random
topologies.

SACA: SCM-based Adaptive Clustering Algorithm,
Yan Li, Snigdha Verma, Li Lao, Jun-Hong Cui,
13th IEEE International Symposium on Modeling, Analysis, and Simulation of
Computer and Telecommunication Systems   pp. 271-279