Abstract
A hybrid of two novel methods - additive fuzzy spectral clustering and lifting method over a taxonomy - is applied to analyse the research activities of a department. To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS), but the approach is applicable also to other taxonomies. Clusters of the taxonomy subjects are extracted using an original additive spectral clustering method involving a number of model-based stopping conditions. The clusters are parsimoniously lifted then to higher ranks of the taxonomy by minimizing the count of “head subjects” along with their “gaps” and “offshoots”. An example is given illustrating the method applied to real-world data.
This is a preview of subscription content, log in via an institution to check access.
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
ACM Computing Classification System (1998), http://www.acm.org/about/class/1998 (Cited September 9, 2008)
Bezdek, J., Keller, J., Krishnapuram, R., Pal, T.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Publishers, Dordrecht (1999)
Feather, M., Menzies, T., Connelly, J.: Matching software practitioner needs to researcher activities. In: Proc. of the 10th Asia-Pacific Software Engineering Conference (APSEC 2003), p. 6. IEEE, Los Alamitos (2003)
Gaevic, D., Hatala, M.: Ontology mappings to improve learning resource search. British Journal of Educational Technology 37(3), 375–389 (2006)
The Gene Ontology Consortium: Gene Ontology: tool for the unification of biology. Nature Genetics 25, 25–29 (2000)
Liu, J., Wang, W., Yang, J.: Gene ontology friendly biclustering of expression profiles. In: Proc. of the IEEE Computational Systems Bioinformatics Conference, pp. 436–447. IEEE, Los Alamitos (2004), doi:10.1109/CSB.2004.1332456
von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17, 395–416 (2007)
Miralaei, S., Ghorbani, A.: Category-based similarity algorithm for semantic similarity in multi-agent information sharing systems. In: IEEE/WIC/ACM Int. Conf. on Intelligent Agent Technology, pp. 242–245 (2005), doi:10.1109/IAT.2005.50
Mirkin, B.: Additive clustering and qualitative factor analysis methods for similarity matrices. Journal of Classification 4(1), 7–31 (1987)
Mirkin, B., Nascimento, S.: Analysis of Community Structure, Affinity Data and Research Activities using Additive Fuzzy Spectral Clustering, Technical Report 6, School of Computer Science, Birkbeck University of London (2009)
Mirkin, B., Nascimento, S., Pereira, L.M.: Cluster-lift method for mapping research activities over a concept tree. In: Koronacki, J., Wierzchon, S.T., Ras, Z.W., Kacprzyk, J. (eds.) Recent Advances in Machine Learning II, Computational Intelligence Series, vol. 263, pp. 245–258. Springer, Heidelberg (2010)
Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Ditterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press, Cambridge (2002)
Graña, M., Torrealdea, F.J.: Hierarchically structured systems. European Journal of Operational Research 25, 20–26 (1986)
Skarman, A., Jiang, L., Hornshoj, H., Buitenhuis, B., Hedegaard, J., Conley, L., Sorensen, P.: Gene set analysis methods applied to chicken microarray expression data. BMC Proceedings 3(Suppl. 4), S8 (2009), doi:10.1186/1753-6561-3-S4-S8
Shepard, R.N., Arabie, P.: Additive clustering: representation of similarities as combinations of overlapping properties. Psychological Review 86, 87–123 (1979)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Thorne, C., Zhu, J., Uren, V.: Extracting domain ontologies with CORDER. Tech. Reportkmi-05-14, pp. 1–15. Open University (2005)
Yang, L., Ball, M., Bhavsar, V., Boley, H.: Weighted partonomy-taxonomy trees with local similarity measures for semantic buyer-seller match-making. Journal of Business and Technology 1(1), 42–52 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mirkin, B., Nascimento, S., Fenner, T., Pereira, L.M. (2010). A Hybrid Cluster-Lift Method for the Analysis of Research Activities. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-13769-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13768-6
Online ISBN: 978-3-642-13769-3
eBook Packages: Computer ScienceComputer Science (R0)