PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automatic determination of the number of clusters using spectral algorithms
Guido Sanguinetti, Jonathan Laidler and Neil Lawrence
In: IEEE Machine Learning for Signal Processing 2005, 28-30 Sept 2005, Mystic, Connecticut, USA.


We introduce a novel spectral clustering algorithm that allows us to automatically determine the number of clusters in a dataset. The algorithm is based on a theoretical analysis of the spectral properties of block diagonal affinity matrices; in contrast to established methods, we do not normalise the rows of the matrix of eigenvectors, and argue that the non-normalised data contains key information that allows the automatic determination of the number of clusters present. We present several examples of datasets successfully clustered by our algorithm, both artificial and real, obtaining good results even without employing refined feature extraction techniques.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1544
Deposited By:Jonathan Laidler
Deposited On:28 November 2005