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Mining of Cell Assay Images Using Active Semi-Supervised-Clustering

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CEBRON, Nicolas, Michael R. BERTHOLD, 2005. Mining of Cell Assay Images Using Active Semi-Supervised-Clustering. In: ICDM, pp. 63-69

@article{Cebron2005Minin-5769, title={Mining of Cell Assay Images Using Active Semi-Supervised-Clustering}, year={2005}, journal={ICDM}, pages={63--69}, author={Cebron, Nicolas and Berthold, Michael R.} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dcterms:isPartOf rdf:resource=""/> <dc:contributor>Cebron, Nicolas</dc:contributor> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:format>application/pdf</dc:format> <dc:date rdf:datatype="">2011-03-24T15:59:56Z</dc:date> <dc:language>eng</dc:language> <dspace:isPartOfCollection rdf:resource=""/> <bibo:uri rdf:resource=""/> <dcterms:bibliographicCitation>First publ. in: ICDM (2005), pp. 63-69</dcterms:bibliographicCitation> <dc:creator>Cebron, Nicolas</dc:creator> <dcterms:available rdf:datatype="">2011-03-24T15:59:56Z</dcterms:available> <dc:creator>Berthold, Michael R.</dc:creator> <dc:contributor>Berthold, Michael R.</dc:contributor> <dcterms:abstract xml:lang="eng">Classifying large datasets without any a-priori information poses a problem especially in the field of bioinformatics. In this work, we explore the problem of classifying hundreds of thousands of cell assay images obtained by a highthroughput screening camera. The goal is to label a few selected examples by hand and to automatically label the rest of the images afterwards. We deal with three major requirements: first, the model should be easy to understand, second it should offer the possibility to be adjusted by a domain expert, and third the interaction with the user should be kept to a minimum. We propose a new active clustering scheme, based on an initial Fuzzy c-means clustering and Learning Vector Quantization. This scheme can initially cluster large datasets unsupervised and then allows for adjustment of the classification by the user. Furthermore, we introduce a framework for the classification of cell assay images based on this technique. Early experiments show promising results.</dcterms:abstract> <dcterms:rights rdf:resource=""/> <dcterms:title>Mining of Cell Assay Images Using Active Semi-Supervised-Clustering</dcterms:title> <dcterms:issued>2005</dcterms:issued> <dcterms:hasPart rdf:resource=""/> <dspace:hasBitstream rdf:resource=""/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> </rdf:Description> </rdf:RDF>

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