%0 Book %1 Barbakh2009 %A Barbakh, Wesam Ashour %A Wu, Ying %A Fyfe, Colin %B Non-Standard Param. Adapt. Explor. Data Anal. %C Berlin, Heidelberg %D 2009 %I Springer Berlin Heidelberg %K clustering survey phd schemdesc %P 7--28 %R 10.1007/978-3-642-04005-4 %T Non-Standard Parameter Adaptation for Exploratory Data Analysis %U http://www.springerlink.com/content/2p67478q4q233w81 %V 249 %X Clustering can be considered the most important unsupervised learning problem; as with every other problem of this kind, it deals with finding structure in a collection of unlabeled data. A cluster is therefore a collection of objects which are “similar” to one another and are “dissimilar” to the objects belonging to other clusters. %@ 978-3-642-04004-7

@book{Barbakh2009, abstract = {Clustering can be considered the most important unsupervised learning problem; as with every other problem of this kind, it deals with finding structure in a collection of unlabeled data. A cluster is therefore a collection of objects which are “similar” to one another and are “dissimilar” to the objects belonging to other clusters.}, added-at = {2013-12-17T10:08:37.000+0100}, address = {Berlin, Heidelberg}, author = {Barbakh, Wesam Ashour and Wu, Ying and Fyfe, Colin}, biburl = {https://www.bibsonomy.org/bibtex/21d1dfe093656ec7ec6ebadd79f73a43d/jullybobble}, booktitle = {Non-Standard Param. Adapt. Explor. Data Anal.}, doi = {10.1007/978-3-642-04005-4}, interhash = {a59949d8408347ab2d85db7179a4a922}, intrahash = {1d1dfe093656ec7ec6ebadd79f73a43d}, isbn = {978-3-642-04004-7}, keywords = {clustering survey phd schemdesc}, mendeley-tags = {clustering,entityguide,schema discovery,survey}, pages = {7--28}, publisher = {Springer Berlin Heidelberg}, series = {Studies in Computational Intelligence}, timestamp = {2014-07-27T15:43:19.000+0200}, title = {{Non-Standard Parameter Adaptation for Exploratory Data Analysis}}, url = {http://www.springerlink.com/content/2p67478q4q233w81}, volume = 249, year = 2009 }