Importance of Dataspace Embeddings when Evaluating Text Clustering Methods
Résumé
Fair evaluation of text clustering methods needs to clarify the relations between 1)pre-processing, 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 transformed dataspaces. 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.
Mots clés
evaluation method
method comparison
text clustering
K-Means
Normalized Matrix Factorization
Latent Dirichlet Allocation
hierarchical clustering
linkage method
spectral clustering
graph partition
kernel clustering
Salton tfidf
Okapi tf-idf
chi-square metrics
Laplacian spectral decomposition
correspondence analysis
kernel expansion
evaluation method
evaluation method
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