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[21450] Artykuł: Microarray Leukemia gene data clustering by means of generalized self-organizing neural networks with evolving tree-like structuresCzasopismo: Lecture Notes in Computer Science Tom: 9119, Strony: 15-25ISSN: 0302-9743 ISBN: 978-3-319-19324-3 Wydawca: SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY Opublikowano: 2015 Seria wydawnicza: Lecture Notes in Artificial Intelligence Autorzy / Redaktorzy / Twórcy Grupa MNiSW: Materiały z konferencji międzynarodowej (zarejestrowane w Web of Science) Punkty MNiSW: 15 Klasyfikacja Web of Science: Proceedings Paper Pełny tekst DOI Web of Science Keywords: microarray cancer gene data  gene expression data clustering  generalized self-organizing neural networks with evolving tree-like structures  cluster analysis  unsupervised learning   |
The paper presents the application of our clustering technique based on generalized self-organizing neural networks with evolving tree-like structures to complex cluster-analysis problems including, in particular, the sample-based and gene-based clusterings of microarray Leukemia gene data set. Our approach works in a fully unsupervised way, i.e., without the necessity to predefine the number of clusters and using unlabelled data. It is particularly important in the gene-based clustering of microarray data for which the number of gene clusters is unknown in advance. In the sample-based clustering of the Leukemia data set, our approach gives better results than those reported in the literature and obtained using a method that requires the cluster number to be defined in advance. In the gene-based clustering of the considered data, our approach generates clusters that are easily divisible into subclusters related to particular sample classes. It corresponds, in a way, to subspace clustering that is highly desirable in microarray data analysis.