| Autor: |
BALBI, SIMONA, SPANO, MARIA, Misuraca, Michelangelo |
| Přispěvatelé: |
Mayaffre, D., Poudat C., Vanni L., Magri V., Follette P., Balbi, Simona, Misuraca, Michelangelo, Spano, Maria |
| Jazyk: |
angličtina |
| Rok vydání: |
2016 |
| Předmět: |
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| Popis: |
Document Clustering is the peculiar application of cluster analysis methods on huge documentary databases. Document Clustering aims at organizing a large quantity of unlabelled documents into a smaller number of meaningful and coherent clusters, similar in content. One of the main unsolved problems in clustering literature is the lack of a reliable methodology to evaluate results, although a wide variety of validation measures has been proposed. If those measures are often unsatisfactory when dealing with numerical databases, they definitely underperform in Document Clustering. This paper proposes a new validation measure. After introducing the most common approaches to Document Clustering, our attention is focused on Spherical K-means, do to its strict connection with the Vector Space Model, typical of Information Retrieval. Since Spherical K-means adopts a cosine-based similarity measure, we propose a validation measure based on the same criterion. The new measure effectiveness is shown in the frame of a comparative study, by involving 13 different corpora (usually used in literature for comparing different proposals) and 15 validation measures. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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