%0 Book %1 sotolongo-aguilar_mining_2001 %A Sotolongo-Aguilar, Gilberto %A Guzman-Sanchez, Maria Victoria %A Saavedra-Fernandez, Oscar %A Carrillo-Calvet, Humberto A %A Davis, Mari %A Wilson, Concepcion S %D 2001 %K (Computer (of BIBLIOMETRICS,DATA information),NEURAL mining,Mapping networks science) %P 665-673 %T Mining informetric data with self-organizing maps %U http://search.ebscohost.com/login.aspx?direct=true&db=lxh&AN=ISTA3702804&site=ehost-live %X Informetric maps are important means for representing qualitative features of data sets, and this practice for multivariate data has been available for some decades. The application of artificial neural networks (ANN) to informetric data is still at a development stage, but nevertheless, unsupervised ANN as Kohonen algorithm (self-organizing maps, or SOM) seems to be a suitable approach for informetric data mining (DM) seeking knowledge discovery (KD). Presents a general bibliometric framework based on the MOBIS Pro-Soft approach, where Viscovery SOMine Enterprise Edition software is used for creating self-organizing maps. Presents a group of informetric-SOM case studies. In each case, dozens of maps were obtained, eliciting features necessary for analysis. These studies can be seen as tasks performed in the framework of technology watch activities using DM and KD approaches. Proceeding Published by University of New South Wales, 2001 Informetric maps are important means for representing qualitative features of data sets, and this practice for multivariate data has been available for some decades. The application of artificial neural networks (ANN) to informetric data is still at a development stage, but nevertheless, unsupervised ANN as Kohonen algorithm (self-organizing maps, or SOM) seems to be a suitable approach for informetric data mining (DM) seeking knowledge discovery (KD). Presents a general bibliometric framework based on the MOBIS Pro-Soft approach, where Viscovery SOMine Enterprise Edition software is used for creating self-organizing maps. Presents a group of informetric-SOM case studies. In each case, dozens of maps were obtained, eliciting features necessary for analysis. These studies can be seen as tasks performed in the framework of technology watch activities using DM and KD approaches. Proceeding Published by University of New South Wales, 2001 %@ 0733418201

@book{sotolongo-aguilar_mining_2001, abstract = {Informetric maps are important means for representing qualitative features of data sets, and this practice for multivariate data has been available for some decades. The application of artificial neural networks (ANN) to informetric data is still at a development stage, but nevertheless, unsupervised ANN as Kohonen algorithm (self-organizing maps, or SOM) seems to be a suitable approach for informetric data mining (DM) seeking knowledge discovery (KD). Presents a general bibliometric framework based on the MOBIS Pro-Soft approach, where Viscovery SOMine Enterprise Edition software is used for creating self-organizing maps. Presents a group of informetric-SOM case studies. In each case, dozens of maps were obtained, eliciting features necessary for analysis. These studies can be seen as tasks performed in the framework of technology watch activities using DM and KD approaches. Proceeding Published by University of New South Wales, 2001 Informetric maps are important means for representing qualitative features of data sets, and this practice for multivariate data has been available for some decades. The application of artificial neural networks (ANN) to informetric data is still at a development stage, but nevertheless, unsupervised ANN as Kohonen algorithm (self-organizing maps, or SOM) seems to be a suitable approach for informetric data mining (DM) seeking knowledge discovery (KD). Presents a general bibliometric framework based on the MOBIS Pro-Soft approach, where Viscovery SOMine Enterprise Edition software is used for creating self-organizing maps. Presents a group of informetric-SOM case studies. In each case, dozens of maps were obtained, eliciting features necessary for analysis. These studies can be seen as tasks performed in the framework of technology watch activities using DM and KD approaches. Proceeding Published by University of New South Wales, 2001}, added-at = {2007-12-03T17:48:07.000+0100}, author = {Sotolongo-Aguilar, Gilberto and Guzman-Sanchez, Maria Victoria and Saavedra-Fernandez, Oscar and Carrillo-Calvet, Humberto A and Davis, Mari and Wilson, Concepcion S}, biburl = {https://www.bibsonomy.org/bibtex/2844d69545dfa055e48f3d136d23e90f3/sercarfe}, interhash = {c56f2bb7cc4e167174429987a657c6da}, intrahash = {844d69545dfa055e48f3d136d23e90f3}, isbn = {0733418201}, keywords = {(Computer (of BIBLIOMETRICS,DATA information),NEURAL mining,Mapping networks science)}, pages = {665-673}, timestamp = {2007-12-03T17:48:19.000+0100}, title = {Mining informetric data with self-organizing maps}, url = {http://search.ebscohost.com/login.aspx?direct=true\&db=lxh\&AN=ISTA3702804\&site=ehost-live }, year = 2001 }