Linear storage and potentially constant time hierarchical clustering using the Baire metric and random spanning paths

Studies in Classification, Data Analysis, and Knowledge Organization

Volumn , Issue , 2016, Pages 43-52

  Murtagh, Fionn ^a,b,c   Contreras, Pedro ^d  

^a UNIVERSITY OF LONDON   (United Kingdom)

^b DE MONTFORT UNIVERSITY   (United Kingdom)

^c UNIVERSITY OF DERBY   (United Kingdom)

^d ThinkingSafe Ltd   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DATA HANDLING;

CONSTANT TIME; HIER-ARCHICAL CLUSTERING; HIGH DIMENSIONAL DATA; LARGE DATASETS; NOVEL VISUALIZATIONS; PERFORMANCE PROPERTIES; RANDOM PERMUTATIONS; RANDOM PROJECTIONS;

DIGITAL STORAGE;

EID: 84981521246     PISSN: 14318814     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-319-25226-1_4     Document Type: Conference Paper

References (10)

1. Boutsidis, C., Zouzias, A., & Drineas, P. (2010). Random projections for k-Means clustering. Advances in Neural Information Processing Systems, 23(3), 298–306.
2. Braunstein, L. A., Zhenhua W. U., Chen, Y., Buldyrev, S. V., Kalisky, T., Sreenivasan, S., Cohen, R., López, E., Havlin, S., & Stanley, H. E. (2007). Optimal path and minimal spanning trees in random weighted networks. International Journal of Bifurcation and Chaos, 17 (7), 2215–2255.
3. Contreras, P., & Murtagh, F. (2012). Fast, linear time hierarchical clustering using the baire metric. Journal of Classification, 29, 118–143.
4. Ferligoj, A., & Batagelj, V. (1982). Clustering with relational constraint. Psychometrika, 47, 413–426.
5. Fern, X. Z., Brodley, C. E. (2003). Random projection for high dimensional data clustering: A cluster ensemble approach. In T. Fawcett & N. Mishra (Eds.), Proceedings 20th International Conference on Machine Learning (pp. 186–193).
6. Kaski, S. (1998). Dimensionality reduction by random mapping: Fast similarity computation for clustering. In IJCNN’98, IEEE International Joint Conference on Neural Networks (Vol. 1, pp. 413–418).
7. Legendre, P., & Legendre, L. (2012). Numerical ecology (3rd ed.). Amsterdam: Elsevier.
8. Manton, K. G., Huang, H. & Xiliang G. U. (2008). Chapter 3 - Molecular basis of CNS aging, frailty, fitness and longevity: A Model based on cellular energetic. In J. P. Tsai (Ed.), Leadingedge cognitive disorders research, New York: Nova Science, Hauppauge.
9. Matrix Market (2013). Matrix market exchange formats, http://math.nist.gov/MatrixMarket/formats.html
10. Murtagh, F. (1985). Multidimensional clustering algorithms. Heidelberg and Vienna: Physica- Verlag.