most citedHierarchical Clustering Based on Mutual Information

101 citations · 115 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.dis-nn20043 cited

Extracting Phases from Aperiodic Signals

Alexander Kraskov, Thomas Kreuz, Ralph G. Andrzejak +3

We demonstrate by means of a simple example that the arbitrariness of defining a phase from an aperiodic signal is not just an academic problem, but is more serious and fundamental…

cond-mat.soft2004

Monte Carlo Protein Folding: Simulations of Met-Enkephalin with Solvent-Accessible Area Parameterizations

Hsiao-Ping Hsu, Bernd A. Berg, Peter Grassberger

Treating realistically the ambient water is one of the main difficulties in applying Monte Carlo methods to protein folding. The solvent-accessible area method, a popular method fo…

cond-mat.soft20045 cited

Sequential Monte Carlo Methods for Protein Folding

Peter Grassberger

We describe a class of growth algorithms for finding low energy states of heteropolymers. These polymers form toy models for proteins, and the hope is that similar methods will ult…

q-bio.QM2003101 cited

Hierarchical Clustering Based on Mutual Information

Alexander Kraskov, Harald Stögbauer, Ralph G. Andrzejak +1

Motivation: Clustering is a frequently used concept in variety of bioinformatical applications. We present a new method for hierarchical clustering of data called mutual informatio…

q-bio.QM20036 cited

Hierarchical Clustering Using Mutual Information

Alexander Kraskov, Harald Stoegbauer, Ralph G. Andrzejak +1

We present a method for hierarchical clustering of data called {\it mutual information clustering} (MIC) algorithm. It uses mutual information (MI) as a similarity measure and expl…