Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Cluster analysis of gene expression data
Cluster analysis of gene expression data
A fast k-means implementation using coresets
Proceedings of the twenty-second annual symposium on Computational geometry
An optimal hierarchical clustering algorithm for gene expression data
Information Processing Letters
Distance functions, clustering algorithms and microarray data analysis
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. Following Handl et al., it can be summarized as a three step process: (a) choice of a distance function; (b) choice of a clustering algorithm; (c) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Unfortunately, the high dimensionality of the data and their noisy nature makes cluster analysis of genomic data particularly difficult. This paper highlights new findings that seem to address a few relevant problems in each of the three mentioned steps, both in regard to the intrinsic predictive power of methods and algorithms and their time performance. Inclusion of this latter aspect into the evaluation process is quite novel, since it is hardly considered in genomic data analysis.