Different clustering techniques means for improved knowledge discovery /
Application of different clustering techniques can result in different basic data set partitions emphasizing diversified aspects of resulting clusters. Since analysts have a great responsibility for the successful interpretation of the results obtained through some of the available tools, and for gi...
Elmentve itt :
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| Dokumentumtípus: | Könyv része |
| Megjelent: |
2010
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| Sorozat: | Proceedings of the Challenges for Analysis of the Economy, the Businesses, and Social Progress : International Scientific Conference Szeged, November 19-21, 2009
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| Kulcsszavak: | Cluster-analízis |
| Online Access: | http://acta.bibl.u-szeged.hu/57808 |
| Tartalmi kivonat: | Application of different clustering techniques can result in different basic data set partitions emphasizing diversified aspects of resulting clusters. Since analysts have a great responsibility for the successful interpretation of the results obtained through some of the available tools, and for giving meaning to what forms a qualitative set of clusters, additional information attained from different tools is of a great use to them. In this article we presented the clustering results of small and medium sized enterprises’ (SMEs) data, obtained in DataEngine, iData Analyzer and Weka tools for intelligent analysis. |
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| Terjedelem/Fizikai jellemzők: | 319-331 |
| ISBN: | 978-963-06-9558-9 |