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Bayesian versus data driven model selection for microarray data

2014

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. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. A…

Clustering Model selection Bayesian information criterion Akaike information criterion Minimum message length BioinformaticsSettore INF/01 - InformaticaComputer sciencebusiness.industryModel selectionBayesian probabilitycomputer.software_genreMachine learningComputer Science ApplicationsData-drivenDetermining the number of clusters in a data setIdentification (information)Bayesian information criterionData miningArtificial intelligenceAkaike information criterionCluster analysisbusinesscomputer
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