A Comparison of Cluster Algorithms and Distance ...
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A Comparison of Cluster Algorithms and Distance ...
Motivation Cluster techniques are commonly used in the analysis of gene expression data to • find co–regulated genes and • groups of genes with similar temporal or spatial expression patterns. We want to investigate the influence of the cluster methods used on the resulting clusters: • different cluster algorithms • various distance measures.
R Package flexclust • Flexible toolbox to investigate the influence of distance measures and cluster algorithms.
• Extensible implementations of the generalized k–means and QT– Clust algorithm.
• Possibility to try out a variety of distance or similarity measures.
• Cluster algorithms are treated separately from distance measures.
• Own distance functions can easily be implemented as distance measures.
R Package flexclust Function kcca uses a family concept similar to the implementation of generalized linear models in S(Chambers and Hastie, 1992). A KCCA family consists of the following two parts:
dist : A function taking N observations and K centroids as inputs and returning the N xK matrix of distances between all observations and centroids.
cent : An (optional) function computing the centroid for a given subset of the observations.
R Package flexclust An example for a new distance measure is given by distJackknife
