Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11890393_41 | ADVIS |
Keywords | Field | DocType |
global knowledge,activation model,whole network,graph theory,different network,sensor network,samcc scheme,experiment evaluation,different level clustering,clustering accuracy,ad hoc network,long term memory,spreading activation | Canopy clustering algorithm,Fuzzy clustering,Data mining,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Artificial intelligence,FLAME clustering,Brown clustering,Cluster analysis,Distributed computing | Conference |
Volume | ISSN | ISBN |
4243 | 0302-9743 | 3-540-46291-0 |
Citations | PageRank | References |
3 | 0.46 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| QingYuan Huang | 1 | 8 | 1.71 |
| Su, Jinshu | 2 | 750 | 96.41 |
| Yingzhi Zeng | 3 | 22 | 5.59 |
| Yongjun Wang | 4 | 27 | 9.19 |