Evaluation of text clustering methods and their dataspace embeddings: an exploration
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 combinations, mostly in spectral or kernel dataspaces. The rich material resulting from these some 450 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
Kernel Clustering
LDA
Latent Dirichlet Allocation
Non-negative Matrix Factorization
NMF
Graph Clustering
Spectral Clustering
Hierarchical Clustering
K-Means
Text Mining
Text Clustering
Clustering Methods
Cluster Analysis
Statistical Evaluation
Benchmark
Okapi
BM25
Correspondence Analysis
chi-square
tf-idf
Reuters' ModApté Split
ACM collection
20 Newsgroups collection
polynomial kernel
Gram matrix
Domaines
Informatique [cs]
Fichier principal
Cadot-Lelu_Cluster-Challenge5.pdf (1.31 Mo)
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PresentationIFCS19LeLu-Cadot_ccc.pdf (995.56 Ko)
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