AN OVERVIEW OF FREQUENTLY USED ALGORITHMS TO BUILD CLUSTERS
Raluca-Mariana Stefan
Pages: 163-174
Published: 1 Jan 2012
Views: 66
Abstract: Unsupervised classification of data is a technique used to determine the natural tendency of data to group and can be applied to predict economic phenomena which are, more or less, stable. Therefore, cluster techniques constitute an area of great interest. Unlike the supervised classification, unsupervised classification proposes an optimal partition of feature space in terms of a certain mathematical criterion, without using apriori information (for example, a reference partition). The advantage of these methods is given that is fully automatic and requires no user intervention; it can be used for classification of data for which we do not have information relative to their content (number of classes, the prototype class etc.).
Keywords: data classification, unsupervised learning, cluster analysis, clustering algorithm
Cite this article: Raluca-Mariana Stefan. AN OVERVIEW OF FREQUENTLY USED ALGORITHMS TO BUILD CLUSTERS. Journal of International Scientific Publications: Materials, Methods & Technologies 6, 163-174 (2012). https://www.scientific-publications.net/en/article/1003204/
Back to the contents of the volume
© 2026 The Author(s). This is an open access article distributed under the terms of the
Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This permission does not cover any third party copyrighted material which may appear in the work requested.
Disclaimer: The Publisher and/or the editor(s) are not responsible for the statements, opinions, and data contained in any published works. These are solely the views of the individual author(s) and contributor(s). The Publisher and/or the editor(s) disclaim any liability for injury to individuals or property arising from the ideas, methods, instructions, or products mentioned in the content.
