A new efficient and unbiased approach for clustering quality evaluation
Identifieur interne : 002342 ( Main/Exploration ); précédent : 002341; suivant : 002343A new efficient and unbiased approach for clustering quality evaluation
Auteurs : Jean-Charles Lamirel [France] ; Pascal Cuxac [France] ; Raghvendra Mall [Inde]Source :
Descripteurs français
Abstract
Traditional quality indexes (Inertia, DB, . . . ) are known to be method-dependent indexes that do not allow to properly estimate the quality of the clustering in several cases, as in that one of complex data, like textual data. We thus propose an alternative approach for clustering quality evaluation based on unsupervised measures of Recall, Precision and F-measure exploiting the descriptors of the data associated with the obtained clusters. Two categories of index are proposed, that are Macro and Micro indexes. This paper also focuses on the construction of a new cumulative Micro precision index that makes it possible to evalu- ate the overall quality of a clustering result while clearly distinguishing between homogeneous and heterogeneous, or degenerated results. The experimental comparison of the behavior of the classical indexes with our new approach is performed on a polythematic dataset of bibliographical references issued from the PASCAL database.
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Affiliations:
- France, Inde
- Grand Est, Lorraine (région)
- Metz, Nancy
- Centre national de la recherche scientifique, Laboratoire lorrain de recherche en informatique et ses applications, Synalp (Loria), Université de Lorraine
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We thus propose an alternative approach for clustering quality evaluation based on unsupervised measures of Recall, Precision and F-measure exploiting the descriptors of the data associated with the obtained clusters. Two categories of index are proposed, that are Macro and Micro indexes. This paper also focuses on the construction of a new cumulative Micro precision index that makes it possible to evalu- ate the overall quality of a clustering result while clearly distinguishing between homogeneous and heterogeneous, or degenerated results. The experimental comparison of the behavior of the classical indexes with our new approach is performed on a polythematic dataset of bibliographical references issued from the PASCAL database.</div></front></TEI><affiliations><list><country><li>France</li><li>Inde</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Centre national de la recherche scientifique</li><li>Laboratoire lorrain de recherche en informatique et ses applications</li><li>Synalp (Loria)</li><li>Université de Lorraine</li></orgName></list><tree><country name="France"><region name="Grand Est"><name sortKey="Lamirel, Jean Charles" sort="Lamirel, Jean Charles" uniqKey="Lamirel J" first="Jean-Charles" last="Lamirel">Jean-Charles Lamirel</name></region><name sortKey="Cuxac, Pascal" sort="Cuxac, Pascal" uniqKey="Cuxac P" first="Pascal" last="Cuxac">Pascal Cuxac</name></country><country name="Inde"><noRegion><name sortKey="Mall, Raghvendra" sort="Mall, Raghvendra" uniqKey="Mall R" first="Raghvendra" last="Mall">Raghvendra Mall</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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