Anzeige
Erschienen in:
2021 | OriginalPaper | Buchkapitel
Evaluation of Text Clustering Methods and Their Dataspace Embeddings: An Exploration
verfasst von : Alain Lelu, Martine Cadot
Erschienen in: Data Analysis and Rationality in a Complex World
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Fair evaluation of text clustering methods needs to clarify the relations between (1) preprocessing, resulting in raw term occurrence vectors, (2) data transformation, and (3) method in the strict sense. We have tried to empirically compare a dozen well-known methods and variants in a protocol crossing three contrasted open-access corpora in a few tens dataspaces with different metrics and/or matrix decompositions. We compared the resulting clusterings to their supposed “ground-truth” classes by means of four usual indices. The results show both a confirmation of well-established implicit combinations and good performances of unexpected ones, mostly in spectral or kernel dataspaces. The rich material resulting from these some 600 runs includes a wealth of intriguing facts, which needs further research on the specificities of text corpora in relation to methods and dataspaces.