PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Associative clustering (AC): Technical details
Janne Sinkkonen, Samuel Kaski, Janne Nikkilä and Leo Lahti
(2005) Technical Report. Helsinki University of Technology, Espoo, Finland.


This report contains derivations which did not fit into the paper \cite{Kaski05tcbb}. Associative clustering (AC) is a method for separately clustering two data sets when one-to-one associations between the sets, implying statistical dependency, are available. AC finds Voronoi partitionings that maximize the visibility of the dependency on the cluster level. The main content of this paper are technical derivations related to the algorithm: A Bayes factor interpretation of AC, gradients for optimizing AC with a smoothing trick, and the connection of AC objective to mutual information.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1700
Deposited By:Samuel Kaski
Deposited On:28 November 2005