Supervised and Unsupervised Classification for Pattern Recognition Purposes

Catalina Cocianu ()

Informatica Economica, 2006, vol. X, issue 4, 5-13

Abstract: A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a mathematical model allowing to express the similarity degree between couples of particular objects, quasi-metrics expressing the similarity between an object an a cluster and between clusters, respectively. In supervised classification, we are provided with a collection of preclassified patterns, and the problem is to label a newly encountered pattern. Typically, the given training patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. The final section of the paper presents a new methodology for supervised learning based on PCA. The classes are represented in the measurement/feature space by a continuous repartitions

Keywords: clustering; supervised classification; pattern recognition; dissimilarity measures; PCA (principal component analysis). (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.revistaie.ase.ro/content/40/cocianu_ie_2006_nr4.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aes:infoec:v:x:y:2006:i:4:p:5-13

Access Statistics for this article

Informatica Economica is currently edited by Ion Ivan

More articles in Informatica Economica from Academy of Economic Studies - Bucharest, Romania Contact information at EDIRC.
Bibliographic data for series maintained by Paul Pocatilu ().