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dc.contributor.authorKim, Se Won
dc.contributor.authorTo, Tang Van
dc.date.accessioned2018-04-17T07:26:37Z
dc.date.available2018-04-17T07:26:37Z
dc.date.issued2013
dc.identifier.citationProceedings of the 2013 5th International Conference on Knowledge and Smart Technology (KST) pages 21-26en_US
dc.identifier.urihttp://repository.au.edu/handle/6623004553/20660
dc.description.abstractThis paper proposes a model of self-growing and self-organizing feature map designed to alleviate the difficulty of predetermining an appropriate size and shape of the feature map suitable for the input data in the applications of the Self-Organizing Map. The proposed model progressively builds a feature map by incremental growing of the network in a way that maintains two-dimensional regular grid structure and gradual adaptation of the reference vectors by coordinated competitive learning dynamics of the Batch Map algorithm. Experimental results based on iris data set and Italian olive oil data set show that the proposed model is effective in discovering an appropriate size and shape of the network grid to manifest a suitable feature map for the input data and that the resultant feature maps are comparable to feature maps produced by the standard SOM algorithm in their quality.
dc.format.extent6 pagesen_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoengen_US
dc.subjectData mining
dc.subjectSelf organizing feature maps
dc.subjectUnsupervised learning
dc.subjectNeural networks (Computer science)
dc.titleA Self-Growing and Self-Organizing Batch Map with Automatic Stopping Conditionen_US
dc.typeTexten_US
dc.rights.holderKim, Se Wonen_US
dc.rights.holderTo, Tang Vanen_US
mods.genreProceeding Paperen_US
au.link.externalLink[Full Text] (https://ieeexplore.ieee.org/document/6512781/)


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