论文信息 - Fast Hierarchical Clustering from the Baire Distance

Fast Hierarchical Clustering from the Baire Distance

The Baire or longest common prefix ultrametric allows a hierarchy, a multiway tree, or ultrametric topology embedding, to be constructed very efficiently. The Baire distance is a 1-bounded ultrametric. For high dimensional data, one approach for the use of the Baire distance is to base the hierarchy construction on random projections. In this paper we use the Baire distance on the Sloan Digital Sky Survey (SDSS, http://www.sdss.org) archive. We are addressing the regression of (high quality, more costly to collect) spectroscopic and (lower quality, more readily available) photometric redshifts. Nonlinear regression is used for mapping photometric and astrometric redshifts.

Fionn Murtagh | Pedro Contreras | F. Murtagh | Pedro Contreras

[1] Heikki Mannila,et al. Random projection in dimensionality reduction: applications to image and text data , 2001, KDD '01.

[2] Wolfgang Gaul,et al. "Classification, Clustering, and Data Mining Applications" , 2004 .

[3] J. A. Hartigan,et al. A k-means clustering algorithm , 1979 .

[4] D. Raffaele,et al. Mining the SDSS archive. I. Photometric redshifts in the nearby universe , 2007, astro-ph/0703108.

[5] G. Longo,et al. Mining the SDSS Archive. I. Photometric Redshifts in the Nearby Universe , 2007 .

[6] Pau Klein,et al. San Francisco, California , 2007 .

[7] Fionn Murtagh,et al. On Ultrametricity, Data Coding, and Computation , 2004, J. Classif..

[8] Stephen Kent. Sloan Digital Sky Survey , 1994 .

[9] Nathan Brown,et al. Chemoinformatics—an introduction for computer scientists , 2009, CSUR.

[10] Fionn Murtagh,et al. Identifying the ultrametricity of time series , 2005 .

[11] Santosh S. Vempala,et al. The Random Projection Method , 2005, DIMACS Series in Discrete Mathematics and Theoretical Computer Science.

[12] Maurice K. Wong,et al. Algorithm AS136: A k-means clustering algorithm. , 1979 .

[13] Fionn Murtagh,et al. Hierarchical Clustering of Massive, High Dimensional Data Sets by Exploiting Ultrametric Embedding , 2008, SIAM J. Sci. Comput..