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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Matula, D.W.: Graph theoretic techniques for cluster analysis algorithms. In: Classification and clustering, pp. 95–129 (1977)
Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1101–1113 (1993)
Dongen, S.: A new cluster algorithm for graphs. Technical report, Amsterdam, The Netherlands (1998)
Ramaswamy, L., Gedik, B., Liu, L.: A distributed approach to node clustering in decentralized peer-to-peer networks. IEEE Trans. Parallel Distrib. Syst. 16(9), 814–829 (2005)
Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76(4-6), 175–181 (2000)
Quillian, M.R.: Semantic memory. In: Collins, A., Smith, E.E. (eds.) Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence, pp. 80–101. Kaufmann, San Mateo (1988)
Crestani, F.: Retrieving documents by constrained spreading activation on automatically constructed hypertexts. In: Fifth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, pp. 1210–1214 (1997)
Atkinson, R.L., Smith, E.E., Nolen-Hoeksema, S.: Introduction to Psychology. Wadsworth Publishing Company, Boston (2002)
Ziegler, C.N., Lausen, G.: Spreading activation models for trust propagation. In: EEE 2004. Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE 2004), Washington, DC, USA, pp. 83–97. IEEE Computer Society, Los Alamitos (2004)
Ceglowski, M., Coburn, A., Cuadrado, J.: Semantic search of unstructured data using contextual network graphs. Technical report, Vermont, USA (2003)
Winick, J., Jamin, S.: Inet-3.0: Internet topology generator. Technical report, University of Michigan, USA (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, Q., Su, J., Zeng, Y., Wang, Y. (2006). Spreading Activation Model for Connectivity Based Clustering. In: Yakhno, T., Neuhold, E.J. (eds) Advances in Information Systems. ADVIS 2006. Lecture Notes in Computer Science, vol 4243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11890393_41
Download citation
DOI: https://doi.org/10.1007/11890393_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46291-0
Online ISBN: 978-3-540-46292-7
eBook Packages: Computer ScienceComputer Science (R0)