[PDF] Analysis of Similarity Measures in Times Series Clustering for the

Correlation Clustering: from Theory to Practice

On Clustering Time Series Using Euclidean Distance and Pearson

Jan 10 2016 Afterwards we show that a properly normalized Euclidean distance is equivalent to a distance based on the Pearson correlation coefficient. After ...

How does gene expression clustering work?

Note that Euclidean distance is sensitive to scaling and differences in average expression level whereas correlation is not. See. Table 1 for these and 

Analysis of Similarity Measures in Times Series Clustering for the

Jan 24 2013 The application of clustering or cluster analysis usually covers one or ... of Euclidean distances and correlations assessed by means of.

Analysis of Similarity Measures in Times Series Clustering for the

Jan 24 2013 The application of clustering or cluster analysis usually covers one or ... of Euclidean distances and correlations assessed by means of.

BMC Bioinformatics

Mar 30 2007 measure the robustness of clustering solutions based on the Euclidean distance

Evaluation of clustering methods for analyzing drug cytokine profiles

Sep 25 2017 Using the Pearson correlation clustering with the average linkage function

Average correlation clustering algorithm (ACCA) for grouping of co

Feb 6 2010 observations. Most of the conventional clustering techniques use. Euclidean and Mahalanobis distances for determining similarity/.

Algorithms: A Comparison of Hierarchical and

different measures of similarity (Euclidean distances vs. Pearson correlation coefficients) on the accuracy of the clustering algorithms was assessed.

scRNAseq clustering tools

In a simple case where you have clearly distinct celltypes simple hierarchical clustering based on euclidean or correlation distances will work fine. Page 44