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Supervised and Unsupervised Classification for Pattern Recognition Purposes

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  • Catalina COCIANU

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

Suggested Citation

  • Catalina COCIANU, 2006. "Supervised and Unsupervised Classification for Pattern Recognition Purposes," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 0(4), pages 5-13.
    • Handle: RePEc:aes:infoec:v:x:y:2006:i:4:p:5-13
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    Cited by:

    1. PREDA Bianca & SERBAN Mariuta & STEFAN Raluca-Mariana, 2013. "Hierarchical Clustering Algorithms And Data Security In Financial Management," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 65(6), pages 147-158.

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